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Ghaderi Daneshmand P, Rabbani H. Total variation regularized tensor ring decomposition for OCT image denoising and super-resolution. Comput Biol Med 2024; 177:108591. [PMID: 38788372 DOI: 10.1016/j.compbiomed.2024.108591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Revised: 04/15/2024] [Accepted: 05/09/2024] [Indexed: 05/26/2024]
Abstract
This paper suggests a novel hybrid tensor-ring (TR) decomposition and first-order tensor-based total variation (FOTTV) model, known as the TRFOTTV model, for super-resolution and noise suppression of optical coherence tomography (OCT) images. OCT imaging faces two fundamental problems undermining correct OCT-based diagnosis: significant noise levels and low sampling rates to speed up the capturing process. Inspired by the effectiveness of TR decomposition in analyzing complicated data structures, we suggest the TRFOTTV model for noise suppression and super-resolution of OCT images. Initially, we extract the nonlocal 3D patches from OCT data and group them to create a third-order low-rank tensor. Subsequently, using TR decomposition, we extract the correlations among all modes of the grouped OCT tensor. Finally, FOTTV is integrated into the TR model to enhance spatial smoothness in OCT images and conserve layer structures more effectively. The proximal alternating minimization and alternative direction method of multipliers are applied to solve the obtained optimization problem. The effectiveness of the suggested method is verified by four OCT datasets, demonstrating superior visual and numerical outcomes compared to state-of-the-art procedures.
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Affiliation(s)
- Parisa Ghaderi Daneshmand
- Medical Image & Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, 8174673461, Iran
| | - Hossein Rabbani
- Medical Image & Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, 8174673461, Iran.
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2
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Yu K, Feng L, Chen Y, Wu M, Zhang Y, Zhu P, Chen W, Wu Q, Hao J. Accurate wavelet thresholding method for ECG signals. Comput Biol Med 2024; 169:107835. [PMID: 38096762 DOI: 10.1016/j.compbiomed.2023.107835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 11/25/2023] [Accepted: 12/05/2023] [Indexed: 02/08/2024]
Abstract
Current wavelet thresholding methods for cardiogram signals captured by flexible wearable sensors face a challenge in achieving both accurate thresholding and real-time signal denoising. This paper proposes a real-time accurate thresholding method based on signal estimation, specifically the normalized ACF, as an alternative to traditional noise estimation without the need for parameter fine-tuning and extensive data training. This method is experimentally validated using a variety of electrocardiogram (ECG) signals from different databases, each containing specific types of noise such as additive white Gaussian (AWG) noise, baseline wander noise, electrode motion noise, and muscle artifact noise. Although this method only slightly outperforms other methods in removing AWG noise in ECG signals, it far outperforms conventional methods in removing other real noise. This is attributed to the method's ability to accurately distinguish not only AWG noise that is significantly different spectrum of the ECG signal, but also real noise with similar spectra. In contrast, the conventional methods are effective only for AWG noise. In additional, this method improves the denoising visualization of the measured ECG signals and can be used to optimize other parameters of other wavelet methods to enhancing the denoised periodic signals, thereby improving diagnostic accuracy.
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Affiliation(s)
- Kaimin Yu
- School of Marine Equipment and Mechanical Engineering, Jimei University, Xiamen, 361021, Fujian, China
| | - Lei Feng
- School of Ocean Information Engineering, Jimei University, Xiamen, 361021, Fujian, China
| | - Yunfei Chen
- School of Ocean Information Engineering, Jimei University, Xiamen, 361021, Fujian, China
| | - Minfeng Wu
- School of Electrical Engineering and Artificial Intelligence, Xiamen University Malaysia, Sepang, 43900, Malaysia
| | - Yuanfang Zhang
- School of Ocean Information Engineering, Jimei University, Xiamen, 361021, Fujian, China
| | - Peibin Zhu
- School of Ocean Information Engineering, Jimei University, Xiamen, 361021, Fujian, China
| | - Wen Chen
- School of Ocean Information Engineering, Jimei University, Xiamen, 361021, Fujian, China.
| | - Qihui Wu
- School of Marine Equipment and Mechanical Engineering, Jimei University, Xiamen, 361021, Fujian, China
| | - Jianzhong Hao
- Institute for Infocomm Research (I(2)R), Agency for Science, Technology and Research (A⋆STAR), 138632, Singapore
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3
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Fang Y, Shao X, Liu B, Lv H. Optical coherence tomography image despeckling based on tensor singular value decomposition and fractional edge detection. Heliyon 2023; 9:e17735. [PMID: 37449117 PMCID: PMC10336597 DOI: 10.1016/j.heliyon.2023.e17735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Revised: 06/26/2023] [Accepted: 06/27/2023] [Indexed: 07/18/2023] Open
Abstract
Optical coherence tomography (OCT) imaging is a technique that is frequently used to diagnose medical conditions. However, coherent noise, sometimes referred to as speckle noise, can dramatically reduce the quality of OCT images, which has an adverse effect on how OCT images are used. In order to enhance the quality of OCT images, a speckle noise reduction technique is developed, and this method is modelled as a low-rank tensor approximation issue. The grouped 3D tensors are first transformed into the transform domain using tensor singular value decomposition (t-SVD). Then, to cut down on speckle noise, transform coefficients are thresholded. Finally, the inverse transform can be used to produce images with speckle suppression. To further enhance the despeckling results, a feature-guided thresholding approach based on fractional edge detection and an adaptive backward projection technique are also presented. Experimental results indicate that the presented algorithm outperforms several comparison methods in relation to speckle suppression, objective metrics, and edge preservation.
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Affiliation(s)
- Ying Fang
- School of Information Technology, Shangqiu Normal University, Shangqiu, 476000, China
| | - Xia Shao
- School of Information Technology, Shangqiu Normal University, Shangqiu, 476000, China
| | - Bangquan Liu
- College of Digital Technology and Engineering, Ningbo University of Finance and Economics, Ningbo, 315100, China
| | - Hongli Lv
- School of Information Technology, Shangqiu Normal University, Shangqiu, 476000, China
- College of Big Data and Software Engineering, Zhejiang Wanli University, Ningbo, 315100, China
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4
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Lee W, Nam HS, Seok JY, Oh WY, Kim JW, Yoo H. Deep learning-based image enhancement in optical coherence tomography by exploiting interference fringe. Commun Biol 2023; 6:464. [PMID: 37117279 PMCID: PMC10147647 DOI: 10.1038/s42003-023-04846-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 04/17/2023] [Indexed: 04/30/2023] Open
Abstract
Optical coherence tomography (OCT), an interferometric imaging technique, provides non-invasive, high-speed, high-sensitive volumetric biological imaging in vivo. However, systemic features inherent in the basic operating principle of OCT limit its imaging performance such as spatial resolution and signal-to-noise ratio. Here, we propose a deep learning-based OCT image enhancement framework that exploits raw interference fringes to achieve further enhancement from currently obtainable optimized images. The proposed framework for enhancing spatial resolution and reducing speckle noise in OCT images consists of two separate models: an A-scan-based network (NetA) and a B-scan-based network (NetB). NetA utilizes spectrograms obtained via short-time Fourier transform of raw interference fringes to enhance axial resolution of A-scans. NetB was introduced to enhance lateral resolution and reduce speckle noise in B-scan images. The individually trained networks were applied sequentially. We demonstrate the versatility and capability of the proposed framework by visually and quantitatively validating its robust performance. Comparative studies suggest that deep learning utilizing interference fringes can outperform the existing methods. Furthermore, we demonstrate the advantages of the proposed method by comparing our outcomes with multi-B-scan averaged images and contrast-adjusted images. We expect that the proposed framework will be a versatile technology that can improve functionality of OCT.
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Affiliation(s)
- Woojin Lee
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Hyeong Soo Nam
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Jae Yeon Seok
- Department of Pathology, Yongin Severance Hospital, Yonsei University College of Medicine, 363 Dongbaekjukjeon-daero, Giheung-gu, Yongin-si, Gyeonggi-do, 16995, Republic of Korea
| | - Wang-Yuhl Oh
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Jin Won Kim
- Multimodal Imaging and Theranostic Lab, Cardiovascular Center, Korea University Guro Hospital, 148 Gurodong-ro, Guro-gu, Seoul, 08308, Republic of Korea
| | - Hongki Yoo
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea.
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Kayadibi İ, Güraksın GE. An Explainable Fully Dense Fusion Neural Network with Deep Support Vector Machine for Retinal Disease Determination. INT J COMPUT INT SYS 2023. [DOI: 10.1007/s44196-023-00210-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/11/2023] Open
Abstract
AbstractRetinal issues are crucial because they result in visual loss. Early diagnosis can aid physicians in initiating treatment and preventing visual loss. Optical coherence tomography (OCT), which portrays retinal morphology cross-sectionally and noninvasively, is used to identify retinal abnormalities. The process of analyzing OCT images, on the other hand, takes time. This study has proposed a hybrid approach based on a fully dense fusion neural network (FD-CNN) and dual preprocessing to identify retinal diseases, such as choroidal neovascularization, diabetic macular edema, drusen from OCT images. A dual preprocessing methodology, in other words, a hybrid speckle reduction filter was initially used to diminish speckle noise present in OCT images. Secondly, the FD-CNN architecture was trained, and the features obtained from this architecture were extracted. Then Deep Support Vector Machine (D-SVM) and Deep K-Nearest Neighbor (D-KNN) classifiers were proposed to reclassify those features and tested on University of California San Diego (UCSD) and Duke OCT datasets. D-SVM demonstrated the best performance in both datasets. D-SVM achieved 99.60% accuracy, 99.60% sensitivity, 99.87% specificity, 99.60% precision and 99.60% F1 score in the UCSD dataset. It achieved 97.50% accuracy, 97.64% sensitivity, 98.91% specificity, 96.61% precision, and 97.03% F1 score in Duke dataset. Additionally, the results were compared to state-of-the-art works on the both datasets. The D-SVM was demonstrated to be an efficient and productive strategy for improving the robustness of automatic retinal disease classification. Also, in this study, it is shown that the unboxing of how AI systems' black-box choices is made by generating heat maps using the local interpretable model-agnostic explanation method, which is an explainable artificial intelligence (XAI) technique. Heat maps, in particular, may contribute to the development of more stable deep learning-based systems, as well as enhancing the confidence in the diagnosis of retinal disease in the analysis of OCT image for ophthalmologists.
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Abbasi A, Monadjemi A, Fang L, Rabbani H, Antony BJ, Ishikawa H. Mixed multiscale BM4D for three-dimensional optical coherence tomography denoising. Comput Biol Med 2023; 155:106658. [PMID: 36827787 PMCID: PMC10739784 DOI: 10.1016/j.compbiomed.2023.106658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Revised: 01/20/2023] [Accepted: 02/09/2023] [Indexed: 02/15/2023]
Abstract
A multiscale extension for the well-known block matching and 4D filtering (BM4D) method is proposed by analyzing and extending the wavelet subbands denoising method in such a way that the proposed method avoids directly denoising detail subbands, which considerably simplifies the computations and makes the multiscale processing feasible in 3D. To this end, we first derive the multiscale construction method in 2D and propose multiscale extensions for three 2D natural image denoising methods. Then, the derivation is extended to 3D by proposing mixed multiscale BM4D (mmBM4D) for optical coherence tomography (OCT) image denoising. We tested mmBM4D on three public OCT datasets captured by various imaging devices. The experiments revealed that mmBM4D significantly outperforms its original counterpart and performs on par with the state-of-the-art OCT denoising methods. In terms of peak-signal-to-noise-ratio (PSNR), mmBM4D surpasses the original BM4D by more than 0.68 decibels over the first dataset. In the second and third datasets, significant improvements in the mean to standard deviation ratio, contrast to noise ratio, and equivalent number of looks were achieved. Furthermore, on the downstream task of retinal layer segmentation, the layer quality preservation of the compared OCT denoising methods is evaluated.
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Affiliation(s)
- Ashkan Abbasi
- Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, USA
| | - Amirhassan Monadjemi
- School of Continuing and Lifelong Education, National University of Singapore, Singapore
| | - Leyuan Fang
- College of Electrical and Information Engineering, Hunan University, China
| | - Hossein Rabbani
- Department of Biomedical Engineering, Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Iran
| | - Bhavna Josephine Antony
- Electrical and Computer System Engineering, Faculty of Engineering, Monash University, Australia; Department of Infectious Diseases, Alfred Health, Australia
| | - Hiroshi Ishikawa
- Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, USA; Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, USA.
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7
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Xie Q, Ma Z, Zhu L, Fan F, Meng X, Gao X, Zhu J. Multi-task generative adversarial network for retinal optical coherence tomography image denoising. Phys Med Biol 2023; 68. [PMID: 36137542 DOI: 10.1088/1361-6560/ac944a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 09/22/2022] [Indexed: 02/07/2023]
Abstract
Objective. Optical coherence tomography (OCT) has become an essential imaging modality for the assessment of ophthalmic diseases. However, speckle noise in OCT images obscures subtle but important morphological details and hampers its clinical applications. In this work, a novel multi-task generative adversarial network (MGAN) is proposed for retinal OCT image denoising.Approach. To strengthen the preservation of retinal structural information in the OCT denoising procedure, the proposed MGAN integrates adversarial learning and multi-task learning. Specifically, the generator of MGAN simultaneously undertakes two tasks, including the denoising task and the segmentation task. The segmentation task aims at the generation of the retinal segmentation map, which can guide the denoising task to focus on the retina-related region based on the retina-attention module. In doing so, the denoising task can enhance the attention to the retinal region and subsequently protect the structural detail based on the supervision of the structural similarity index measure loss.Main results. The proposed MGAN was evaluated and analyzed on three public OCT datasets. The qualitative and quantitative comparisons show that the MGAN method can achieve higher image quality, and is more effective in both speckle noise reduction and structural information preservation than previous denoising methods.Significance. We have presented a MGAN for retinal OCT image denoising. The proposed method provides an effective way to strengthen the preservation of structural information while suppressing speckle noise, and can promote the OCT applications in the clinical observation and diagnosis of retinopathy.
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Affiliation(s)
- Qiaoxue Xie
- Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing 100192, People's Republic of China.,Beijing Laboratory of Biomedical Testing Technology and Instruments, Beijing Information Science and Technology University, Beijing 100192, People's Republic of China
| | - Zongqing Ma
- Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing 100192, People's Republic of China.,Beijing Laboratory of Biomedical Testing Technology and Instruments, Beijing Information Science and Technology University, Beijing 100192, People's Republic of China
| | - Lianqing Zhu
- Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing 100192, People's Republic of China.,Beijing Laboratory of Biomedical Testing Technology and Instruments, Beijing Information Science and Technology University, Beijing 100192, People's Republic of China
| | - Fan Fan
- Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing 100192, People's Republic of China.,Beijing Laboratory of Biomedical Testing Technology and Instruments, Beijing Information Science and Technology University, Beijing 100192, People's Republic of China
| | - Xiaochen Meng
- Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing 100192, People's Republic of China.,Beijing Laboratory of Biomedical Testing Technology and Instruments, Beijing Information Science and Technology University, Beijing 100192, People's Republic of China
| | - Xinxiao Gao
- Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, People's Republic of China
| | - Jiang Zhu
- Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing 100192, People's Republic of China.,Beijing Laboratory of Biomedical Testing Technology and Instruments, Beijing Information Science and Technology University, Beijing 100192, People's Republic of China
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8
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Chen H, Gao J. Non-Local Mean Denoising Algorithm Based on Fractional Compact Finite Difference Scheme Effectively Reduces Speckle Noise in Optical Coherence Tomography Images. MICROMACHINES 2022; 13:2039. [PMID: 36557339 PMCID: PMC9781262 DOI: 10.3390/mi13122039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 11/18/2022] [Accepted: 11/19/2022] [Indexed: 06/17/2023]
Abstract
Optical coherence tomography (OCT) is used in various fields such, as medical diagnosis and material inspection, as a non-invasive and high-resolution optical imaging modality. However, an OCT image is damaged by speckle noise during its generation, thus reducing the image quality. To address this problem, a non-local means (NLM) algorithm based on the fractional compact finite difference scheme (FCFDS) is proposed to remove the speckle noise in OCT images. FCFDS uses more local pixel information when compared to integer-order difference operators. The FCFDS operator is introduced into the NLM algorithm to construct a high-precision weight calculation so that the proposed algorithm can effectively reduce the speckle noise in the OCT images. Experiments on simulations and real OCT images show that the proposed method is comparable to other state-of-the-art despeckling methods and can substantially reduce noise and preserve image details such as edges and structures. Speckle noise removal can further promote the application of the proposed algorithm in medical diagnosis and industrial detection, as it has key research value.
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Affiliation(s)
- Huaiguang Chen
- School of Science, Shandong Jianzhu University, Jinan 250101, China
- Center for Engineering Computation and Software Development, Shandong Jianzhu University, Jinan 250101, China
| | - Jing Gao
- School of Science, Shandong Jianzhu University, Jinan 250101, China
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Li Z, Poon W, Ye Z, Qi F, Park BH, Yin Y. Magnetic Field-Modulated Plasmonic Scattering of Hybrid Nanorods for FFT-Weighted OCT Imaging in NIR-II. ACS NANO 2022; 16:12738-12746. [PMID: 35925674 DOI: 10.1021/acsnano.2c04590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
We report a method for fast Fourier transform (FFT)-weighted optical coherence tomography (OCT) in the second biological tissue transparency window by actively modulating the plasmonic scattering of Fe3O4@Au hybrid nanorods using magnetic fields. Instead of tracking the nanoparticles' lateral displacement in conventional magnetomotive OCT imaging, we monitor the nanorod rotation and optical signal changes under an alternating magnetic field in real time. The coherent rotation of the nanorods with the field produces periodic OCT signals, and the FFT is then used to convert the periodic OCT signals in the time domain to a single peak in the frequency domain. This allows automatic screening of nanorod signals from the random biological noises and reconstruction of FFT-weighted images using a computer program based on a time-sequence image set. Compared with conventional magnetomotive OCT, the FFT-weighted imaging technique creates enhanced OCT images with dB-scale contrast over an order of magnitude higher than the original images.
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Affiliation(s)
- Zhiwei Li
- Department of Chemistry, University of California, Riverside, California 92521, United States
| | - Wesley Poon
- Department of Bioengineering, University of California, Riverside, California 92521, United States
| | - Zuyang Ye
- Department of Chemistry, University of California, Riverside, California 92521, United States
| | - Fenglian Qi
- Department of Chemistry, University of California, Riverside, California 92521, United States
| | - B Hyle Park
- Department of Bioengineering, University of California, Riverside, California 92521, United States
| | - Yadong Yin
- Department of Chemistry, University of California, Riverside, California 92521, United States
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10
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Varadarajan D, Magnain C, Fogarty M, Boas DA, Fischl B, Wang H. A novel algorithm for multiplicative speckle noise reduction in ex vivo human brain OCT images. Neuroimage 2022; 257:119304. [PMID: 35568350 PMCID: PMC10018743 DOI: 10.1016/j.neuroimage.2022.119304] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 05/06/2022] [Accepted: 05/10/2022] [Indexed: 10/18/2022] Open
Abstract
Optical coherence tomography (OCT) images of ex vivo human brain tissue are corrupted by multiplicative speckle noise that degrades the contrast to noise ratio (CNR) of microstructural compartments. This work proposes a novel algorithm to reduce noise corruption in OCT images that minimizes the penalized negative log likelihood of gamma distributed speckle noise. The proposed method is formulated as a majorize-minimize problem that reduces to solving an iterative regularized least squares optimization. We demonstrate the usefulness of the proposed method by removing speckle in simulated data, phantom data and real OCT images of human brain tissue. We compare the proposed method with state of the art filtering and non-local means based denoising methods. We demonstrate that our approach removes speckle accurately, improves CNR between different tissue types and better preserves small features and edges in human brain tissue.
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Affiliation(s)
- Divya Varadarajan
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA; Radiology, Harvard Medical School, Boston, MA 02115, USA.
| | - Caroline Magnain
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA; Radiology, Harvard Medical School, Boston, MA 02115, USA
| | - Morgan Fogarty
- Imaging Science Program, Washington University McKelvey School of Engineering, St. Louis, MO 63130, USA; Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - David A Boas
- Biomedical Engineering and Electrical and Computer Engineering, Boston University, Boston, MA 02215, USA
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA; Radiology, Harvard Medical School, Boston, MA 02115, USA; Harvard-MIT Health Science and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Hui Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA; Radiology, Harvard Medical School, Boston, MA 02115, USA
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11
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Su H, Kwok KW, Cleary K, Iordachita I, Cavusoglu MC, Desai JP, Fischer GS. State of the Art and Future Opportunities in MRI-Guided Robot-Assisted Surgery and Interventions. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2022; 110:968-992. [PMID: 35756185 PMCID: PMC9231642 DOI: 10.1109/jproc.2022.3169146] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Magnetic resonance imaging (MRI) can provide high-quality 3-D visualization of target anatomy, surrounding tissue, and instrumentation, but there are significant challenges in harnessing it for effectively guiding interventional procedures. Challenges include the strong static magnetic field, rapidly switching magnetic field gradients, high-power radio frequency pulses, sensitivity to electrical noise, and constrained space to operate within the bore of the scanner. MRI has a number of advantages over other medical imaging modalities, including no ionizing radiation, excellent soft-tissue contrast that allows for visualization of tumors and other features that are not readily visible by other modalities, true 3-D imaging capabilities, including the ability to image arbitrary scan plane geometry or perform volumetric imaging, and capability for multimodality sensing, including diffusion, dynamic contrast, blood flow, blood oxygenation, temperature, and tracking of biomarkers. The use of robotic assistants within the MRI bore, alongside the patient during imaging, enables intraoperative MR imaging (iMRI) to guide a surgical intervention in a closed-loop fashion that can include tracking of tissue deformation and target motion, localization of instrumentation, and monitoring of therapy delivery. With the ever-expanding clinical use of MRI, MRI-compatible robotic systems have been heralded as a new approach to assist interventional procedures to allow physicians to treat patients more accurately and effectively. Deploying robotic systems inside the bore synergizes the visual capability of MRI and the manipulation capability of robotic assistance, resulting in a closed-loop surgery architecture. This article details the challenges and history of robotic systems intended to operate in an MRI environment and outlines promising clinical applications and associated state-of-the-art MRI-compatible robotic systems and technology for making this possible.
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Affiliation(s)
- Hao Su
- Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC 27695 USA
| | - Ka-Wai Kwok
- Department of Mechanical Engineering, The University of Hong Kong, Hong Kong
| | - Kevin Cleary
- Children's National Health System, Washington, DC 20010 USA
| | - Iulian Iordachita
- Laboratory for Computational Sensing and Robotics (LCSR), Johns Hopkins University, Baltimore, MD 21218 USA
| | - M Cenk Cavusoglu
- Department of Electrical, Computer, and Systems Engineering, Case Western Reserve University, Cleveland, OH 44106 USA
| | - Jaydev P Desai
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA
| | - Gregory S Fischer
- Department of Robotics Engineering, Worcester Polytechnic Institute, Worcester, MA 01609 USA
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12
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Zhou Y, Li J, Wang M, Peng Y, Chen Z, Zhu W, Shi F, Wang L, Wang T, Yao C, Chen X. DHNet: High-resolution and hierarchical network for cross-domain oct speckle noise reduction. Med Phys 2022; 49:5914-5928. [PMID: 35611567 DOI: 10.1002/mp.15712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 03/26/2022] [Accepted: 05/03/2022] [Indexed: 11/05/2022] Open
Abstract
PURPOSE Optical coherence tomography (OCT) imaging uses the principle of Michelson interferometry to obtain high-resolution images by coherent superposing of multiple forward and backward scattered light waves with random phases. This process inevitably produces speckle noise that severely compromises visual quality of OCT images and degrades performances of subsequent image analysis tasks. In addition, datasets obtained by different OCT scanners have distribution shifts, making a speckle noise suppression model difficult to be generalized across multiple datasets. In order to solve the above issues, we propose a novel end-to-end denoising framework for OCT images collected by different scanners. METHODS The proposed model utilizes the high-resolution network (HRNet) as backbone for image restoration, which reconstructs high fidelity images by maintaining high-resolution representations throughout the entire learning process. To compensate distribution shifts among datasets collected by different scanners, we develop a hierarchical adversarial learning strategy for domain adaption. The proposed model is trained using datasets with clean ground truth produced by two commercial OCT scanners, and then applied to suppress speckle noise in OCT images collected by our recently developed OCT scanner, BV-1000 (China Bigvision Corporation). We name the proposed model as DHNet (Double-H-Net, High-resolution and Hierarchical Network). RESULTS We compare DHNet with state-of-the-art methods and experiment results show that DHNet improves signal-to-noise ratio (SNR) by a large margin of 18.137dB as compared to the best of our previous method. In addition, DHNet achieves a testing time of 25ms, which satisfies the real-time processing requirement for the BV-1000 scanner. We also conduct retinal layer segmentation experiment on OCT images before and after denoising and show that DHNet can also improve segmentation. CONCLUSIONS The proposed DHNet can compensate domain shifts between different datasets while significantly improve speckle noise suppression. The HRNet backbone is utilized to carry low- and high-resolution information to recover fidelity images. Domain adaptation is achieved by a hierarchical module through adversarial learning. In addition, DHNet achieved a testing time of 25ms, which satisfied the real-time processing requirement. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Yi Zhou
- School of Electronics and Information Engineering, Soochow University, No.1 Shizi Street, Suzhou, Jiangsu, 215006, China
| | - Jiang Li
- Department of Electrical and Computer Engineering, Old Dominion University, 231D Kaufman Hall, Norfolk, VA, 23529, USA
| | - Meng Wang
- School of Electronics and Information Engineering, Soochow University, No.1 Shizi Street, Suzhou, Jiangsu, 215006, China
| | - Yuanyuan Peng
- School of Electronics and Information Engineering, Soochow University, No.1 Shizi Street, Suzhou, Jiangsu, 215006, China
| | - Zhongyue Chen
- School of Electronics and Information Engineering, Soochow University, No.1 Shizi Street, Suzhou, Jiangsu, 215006, China
| | - Weifang Zhu
- School of Electronics and Information Engineering, Soochow University, No.1 Shizi Street, Suzhou, Jiangsu, 215006, China
| | - Fei Shi
- School of Electronics and Information Engineering, Soochow University, No.1 Shizi Street, Suzhou, Jiangsu, 215006, China
| | - Lianyu Wang
- School of Electronics and Information Engineering, Soochow University, No.1 Shizi Street, Suzhou, Jiangsu, 215006, China
| | - Tingting Wang
- School of Electronics and Information Engineering, Soochow University, No.1 Shizi Street, Suzhou, Jiangsu, 215006, China
| | - Chenpu Yao
- School of Electronics and Information Engineering, Soochow University, No.1 Shizi Street, Suzhou, Jiangsu, 215006, China
| | - Xinjian Chen
- School of Electronics and Information Engineering, Soochow University, No.1 Shizi Street, Suzhou, Jiangsu, 215006, China.,State Key Laboratory of Radiation Medicine and Protection, Soochow University, Suzhou, 215006, China
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Ni G, Wu R, Zhong J, Chen Y, Wan L, Xie Y, Mei J, Liu Y. Hybrid-structure network and network comparative study for deep-learning-based speckle-modulating optical coherence tomography. OPTICS EXPRESS 2022; 30:18919-18938. [PMID: 36221682 DOI: 10.1364/oe.454504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Accepted: 04/26/2022] [Indexed: 06/16/2023]
Abstract
Optical coherence tomography (OCT), a promising noninvasive bioimaging technique, can resolve sample three-dimensional microstructures. However, speckle noise imposes obvious limitations on OCT resolving capabilities. Here we proposed a deep-learning-based speckle-modulating OCT based on a hybrid-structure network, residual-dense-block U-Net generative adversarial network (RDBU-Net GAN), and further conducted a comprehensively comparative study to explore multi-type deep-learning architectures' abilities to extract speckle pattern characteristics and remove speckle, and resolve microstructures. This is the first time that network comparative study has been performed on a customized dataset containing mass more-general speckle patterns obtained from a custom-built speckle-modulating OCT, but not on retinal OCT datasets with limited speckle patterns. Results demonstrated that the proposed RDBU-Net GAN has a more excellent ability to extract speckle pattern characteristics and remove speckle, and resolve microstructures. This work will be useful for future studies on OCT speckle removing and deep-learning-based speckle-modulating OCT.
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Zhang X, Li Z, Nan N, Wang X. Denoising algorithm of OCT images via sparse representation based on noise estimation and global dictionary. OPTICS EXPRESS 2022; 30:5788-5802. [PMID: 35209533 DOI: 10.1364/oe.447668] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 01/28/2022] [Indexed: 06/14/2023]
Abstract
Optical coherence tomography (OCT) is a high-resolution and non-invasive optical imaging technology, which is widely used in many fields. Nevertheless, OCT images are disturbed by speckle noise due to the low-coherent interference properties of light, resulting in significant degradation of OCT image quality. Therefore, a denoising algorithm of OCT images via sparse representation based on noise estimation and global dictionary is proposed in this paper. To remove noise and improve image quality, the algorithm first constructs a global dictionary from high-quality OCT images as training samples and then estimates the noise intensity for each input image. Finally, the OCT images are sparsely decomposed and reconstructed according to the global dictionary and noise intensity. Experimental results indicate that the proposed algorithm efficiently removes speckle noise from OCT images and yield high-quality images. The denoising effect and execution efficiency are evaluated based on quantitative metrics and running time, respectively. Compared with the mainstream adaptive dictionary denoising algorithm in sparse representation and other denoising algorithms, the proposed algorithm exhibits satisfying results in terms of speckle-noise reduction as well as edge preservation, at a reduced computational cost. Moreover, the final denoising effect is significantly better for sets of images with significant variations in noise intensity.
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Fan F, Zhang J, Zhu L, Ma Z, Zhu J. Improving cerebral microvascular image quality of optical coherence tomography angiography with deep learning-based segmentation. JOURNAL OF BIOPHOTONICS 2021; 14:e202100171. [PMID: 34382744 DOI: 10.1002/jbio.202100171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 08/07/2021] [Accepted: 08/09/2021] [Indexed: 06/13/2023]
Abstract
Optical coherence tomography angiography (OCTA) can map the microvascular networks of the cerebral cortices with micrometer resolution and millimeter penetration. However, the high scattering of the skull and the strong noise in the deep imaging region will distort the vasculature projections and decrease the OCTA image quality. Here, we proposed a deep learning-based segmentation method based on a U-Net convolutional neural network to extract the cortical region from the OCT image. The vascular networks were then visualized by three OCTA algorithms. The image quality of the vasculature projections was assessed by two metrics, including the peak signal-to-noise ratio (PSNR) and the contrast-to-noise ratio (CNR). The results show the accuracy of the cortical segmentation was 96.07%. The PSNR and CNR values increased significantly in the projections of the selected cortical regions. The OCTA incorporating the deep learning-based cortical segmentation can efficiently improve the image quality and enhance the vasculature clarity.
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Affiliation(s)
- Fan Fan
- Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing, China
- Beijing Laboratory for Biomedical Detection Technology and Instrument, Beijing Information Science and Technology University, Beijing, China
| | - Jisheng Zhang
- Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing, China
- Beijing Laboratory for Biomedical Detection Technology and Instrument, Beijing Information Science and Technology University, Beijing, China
| | - Lianqing Zhu
- Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing, China
- Beijing Laboratory for Biomedical Detection Technology and Instrument, Beijing Information Science and Technology University, Beijing, China
| | - Zongqing Ma
- Beijing Laboratory for Biomedical Detection Technology and Instrument, Beijing Information Science and Technology University, Beijing, China
| | - Jiang Zhu
- Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing, China
- Beijing Laboratory for Biomedical Detection Technology and Instrument, Beijing Information Science and Technology University, Beijing, China
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Huang Y, Xia W, Lu Z, Liu Y, Chen H, Zhou J, Fang L, Zhang Y. Noise-Powered Disentangled Representation for Unsupervised Speckle Reduction of Optical Coherence Tomography Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:2600-2614. [PMID: 33326376 DOI: 10.1109/tmi.2020.3045207] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Due to its noninvasive character, optical coherence tomography (OCT) has become a popular diagnostic method in clinical settings. However, the low-coherence interferometric imaging procedure is inevitably contaminated by heavy speckle noise, which impairs both visual quality and diagnosis of various ocular diseases. Although deep learning has been applied for image denoising and achieved promising results, the lack of well-registered clean and noisy image pairs makes it impractical for supervised learning-based approaches to achieve satisfactory OCT image denoising results. In this paper, we propose an unsupervised OCT image speckle reduction algorithm that does not rely on well-registered image pairs. Specifically, by employing the ideas of disentangled representation and generative adversarial network, the proposed method first disentangles the noisy image into content and noise spaces by corresponding encoders. Then, the generator is used to predict the denoised OCT image with the extracted content features. In addition, the noise patches cropped from the noisy image are utilized to facilitate more accurate disentanglement. Extensive experiments have been conducted, and the results suggest that our proposed method is superior to the classic methods and demonstrates competitive performance to several recently proposed learning-based approaches in both quantitative and qualitative aspects. Code is available at: https://github.com/tsmotlp/DRGAN-OCT.
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Noise reduction by adaptive-SIN filtering for retinal OCT images. Sci Rep 2021; 11:19498. [PMID: 34593894 PMCID: PMC8484270 DOI: 10.1038/s41598-021-98832-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 09/13/2021] [Indexed: 11/17/2022] Open
Abstract
Optical coherence tomography (OCT) images is widely used in ophthalmic examination, but their qualities are often affected by noises. Shearlet transform has shown its effectiveness in removing image noises because of its edge-preserving property and directional sensitivity. In the paper, we propose an adaptive denoising algorithm for OCT images. The OCT noise is closer to the Poisson distribution than the Gaussian distribution, and shearlet transform assumes additive white Gaussian noise. We hence propose a square-root transform to redistribute the OCT noise. Different manufacturers and differences between imaging objects may influence the observed noise characteristics, which make predefined thresholding scheme ineffective. We propose an adaptive 3D shearlet image filter with noise-redistribution (adaptive-SIN) scheme for OCT images. The proposed adaptive-SIN is evaluated on three benchmark datasets using quantitative evaluation metrics and subjective visual inspection. Compared with other algorithms, the proposed algorithm better removes noise in OCT images and better preserves image details, significantly outperforming in terms of both quantitative evaluation and visual inspection. The proposed algorithm effectively transforms the Poisson noise to Gaussian noise so that the subsequent shearlet transform could optimally remove the noise. The proposed adaptive thresholding scheme optimally adapts to various noise conditions and hence better remove the noise. The comparison experimental results on three benchmark datasets against 8 compared algorithms demonstrate the effectiveness of the proposed approach in removing OCT noise.
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Wu M, Chen W, Chen Q, Park H. Noise Reduction for SD-OCT Using a Structure-Preserving Domain Transfer Approach. IEEE J Biomed Health Inform 2021; 25:3460-3472. [PMID: 33822730 DOI: 10.1109/jbhi.2021.3071421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Spectral-domain optical coherence tomography (SD-OCT) images inevitably suffer from multiplicative speckle noise caused by random interference. This study proposes an unsupervised domain adaptation approach for noise reduction by translating the SD-OCT to the corresponding high-quality enhanced depth imaging (EDI)-OCT. We propose a structure-persevered cycle-consistent generative adversarial network for unpaired image-to-image translation, which can be applied to imbalanced unpaired data, and can effectively preserve retinal details based on a structure-specific cross-domain description. It also imposes smoothness by penalizing the intensity variation of the low reflective region between consecutive slices. Our approach was tested on a local data set that consisted of 268 SD-OCT volumes and two public independent validation datasets including 20 SD-OCT volumes and 17 B-scans, respectively. Experimental results show that our method can effectively suppress noise and maintain the retinal structure, compared with other traditional approaches and deep learning methods in terms of qualitative and quantitative assessments. Our proposed method shows good performance for speckle noise reduction and can assist downstream tasks of OCT analysis.
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Ng DSC, Chan LKY, Ng CM, Lai TYY. Visualising the choriocapillaris: Histology, imaging modalities and clinical research - A review. Clin Exp Ophthalmol 2021; 50:91-103. [PMID: 34387023 DOI: 10.1111/ceo.13984] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 08/09/2021] [Indexed: 01/02/2023]
Abstract
The choriocapillaris plays a considerable role in the normal physiology of the eye as well as in various diseases. Assessing the changes in the choriocapillaris can therefore provide important information about normal ageing and pathogenesis of visual impairment, and even some systemic diseases. In vivo imaging of the choriocapillaris has evolved from non-depth resolved, dye-based angiography to advanced, high-resolution optical coherence tomography angiography (OCTA). However, the intricate microvascular networks within the choriocapillaris are still beyond the resolving limits of most OCTA instruments. Knowledge of histology, meticulous image acquisition methods, recognition of artefact and post-acquisition processing techniques are necessary for optimising OCTA choriocapillaris images. Qualitative and quantitative analyses of the choriocapillaris provide clinical information in age-related macular degeneration (AMD), diabetic retinopathy (DR), pathologic myopia and central serous chorioretinopathy (CSC). Furthermore, studies have revealed choriocapillaris changes in posterior uveitis that are correlated with treatment outcome and have important prognostic significance. In addition to retinal diseases, choriocapillaris changes have been observed in systemic vascular diseases and complications associated with pregnancy.
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Affiliation(s)
- Danny Siu-Chun Ng
- Department of Ophthalmology & Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China.,Hong Kong Eye Hospital, Mong Kok, Hong Kong
| | - Leo Ka-Yu Chan
- Department of Ophthalmology & Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China.,Hong Kong Eye Hospital, Mong Kok, Hong Kong
| | - Ching Man Ng
- Department of Ophthalmology & Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Timothy Y Y Lai
- Department of Ophthalmology & Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China.,2010 Retina & Macula Centre, Kowloon, Hong Kong
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20
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Ni G, Chen Y, Wu R, Wang X, Zeng M, Liu Y. Sm-Net OCT: a deep-learning-based speckle-modulating optical coherence tomography. OPTICS EXPRESS 2021; 29:25511-25523. [PMID: 34614881 DOI: 10.1364/oe.431475] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
Speckle imposes obvious limitations on resolving capabilities of optical coherence tomography (OCT), while speckle-modulating OCT can efficiently reduce speckle arbitrarily. However, speckle-modulating OCT seriously reduces the imaging sensitivity and temporal resolution of the OCT system when reducing speckle. Here, we proposed a deep-learning-based speckle-modulating OCT, termed Sm-Net OCT, by deeply integrating conventional OCT setup and generative adversarial network trained with a customized large speckle-modulating OCT dataset containing massive speckle patterns. The customized large speckle-modulating OCT dataset was obtained from the aforementioned conventional OCT setup rebuilt into a speckle-modulating OCT and performed imaging using different scanning parameters. Experimental results demonstrated that the proposed Sm-Net OCT can effectively obtain high-quality OCT images without the electronic noise and speckle, and conquer the limitations of reducing the imaging sensitivity and temporal resolution which conventional speckle-modulating OCT has. The proposed Sm-Net OCT can significantly improve the adaptability and practicality capabilities of OCT imaging, and expand its application fields.
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21
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Gómez-Valverde JJ, Sinz C, Rank EA, Chen Z, Santos A, Drexler W, Ledesma-Carbayo MJ. Adaptive compounding speckle-noise-reduction filter for optical coherence tomography images. JOURNAL OF BIOMEDICAL OPTICS 2021; 26:JBO-210051R. [PMID: 34142472 PMCID: PMC8211087 DOI: 10.1117/1.jbo.26.6.065001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Accepted: 05/24/2021] [Indexed: 06/12/2023]
Abstract
SIGNIFICANCE Speckle noise limits the diagnostic capabilities of optical coherence tomography (OCT) images, causing both a reduction in contrast and a less accurate assessment of the microstructural morphology of the tissue. AIM We present a speckle-noise reduction method for OCT volumes that exploits the advantages of adaptive-noise wavelet thresholding with a wavelet compounding method applied to several frames acquired from consecutive positions. The method takes advantage of the wavelet representation of the speckle statistics, calculated properly from a homogeneous sample or a region of the noisy volume. APPROACH The proposed method was first compared quantitatively with different state-of-the-art approaches by being applied to three different clinical dermatological OCT volumes with three different OCT settings. The method was also applied to a public retinal spectral-domain OCT dataset to demonstrate its applicability to different imaging modalities. RESULTS The results based on four different metrics demonstrate that the proposed method achieved the best performance among the tested techniques in suppressing noise and preserving structural information. CONCLUSIONS The proposed OCT denoising technique has the potential to adapt to different image OCT settings and noise environments and to improve image quality prior to clinical diagnosis based on visual assessment.
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Affiliation(s)
- Juan J. Gómez-Valverde
- Universidad Politécnica de Madrid, ETSI Telecomunicación, Biomedical Image Technologies Laboratory, Madrid, Spain
- Biomedical Research Center in Bioengineering, Biomaterials and Nanomedicine, Madrid, Spain
| | - Christoph Sinz
- Medical University of Vienna, Department of Dermatology, Vienna, Austria
| | - Elisabet A. Rank
- Medical University of Vienna, Center for Medical Physics and Biomedical Engineering, Vienna, Austria
| | - Zhe Chen
- Medical University of Vienna, Center for Medical Physics and Biomedical Engineering, Vienna, Austria
| | - Andrés Santos
- Universidad Politécnica de Madrid, ETSI Telecomunicación, Biomedical Image Technologies Laboratory, Madrid, Spain
- Biomedical Research Center in Bioengineering, Biomaterials and Nanomedicine, Madrid, Spain
| | - Wolfgang Drexler
- Medical University of Vienna, Center for Medical Physics and Biomedical Engineering, Vienna, Austria
| | - María J. Ledesma-Carbayo
- Universidad Politécnica de Madrid, ETSI Telecomunicación, Biomedical Image Technologies Laboratory, Madrid, Spain
- Biomedical Research Center in Bioengineering, Biomaterials and Nanomedicine, Madrid, Spain
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Zhou Y, Yu K, Wang M, Ma Y, Peng Y, Chen Z, Zhu W, Shi F, Chen X. Speckle Noise Reduction for OCT Images based on Image Style Transfer and Conditional GAN. IEEE J Biomed Health Inform 2021; 26:139-150. [PMID: 33882009 DOI: 10.1109/jbhi.2021.3074852] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Raw optical coherence tomography (OCT) images typically are of low quality because speckle noise blurs retinal structures, severely compromising visual quality and degrading performances of subsequent image analysis tasks. In our previous study, we have developed a Conditional Generative Adversarial Network (cGAN) for speckle noise removal in OCT images collected by several commercial OCT scanners, which we collectively refer to as scanner T. In this paper, we improve the cGAN model and apply it to our in-house OCT scanner (scanner B) for speckle noise suppression. The proposed model consists of two steps: 1) We train a Cycle-Consistent GAN (CycleGAN) to learn style transfer between two OCT image datasets collected by different scanners. The purpose of the CycleGAN is to leverage the ground truth dataset created in our previous study. 2) We train a mini-cGAN model based on the PatchGAN mechanism with the ground truth dataset to suppress speckle noise in OCT images. After training, we first apply the CycleGAN model to convert raw images collected by scanner B to match the style of the images from scanner T, and subsequently use the mini-cGAN model to suppress speckle noise in the style transferred images. We evaluate the proposed method on a dataset collected by scanner B. Experimental results show that the improved model outperforms our previous method and other state-of-the-art models in speckle noise removal, retinal structure preservation and contrast enhancement.
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Anoop B, Kalmady KS, Udathu A, Siddharth V, Girish G, Kothari AR, Rajan J. A cascaded convolutional neural network architecture for despeckling OCT images. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102463] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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Lou S, Chen X, Liu J, Shi Y, Qu H, Wang Y, Cai H. Fast OCT image enhancement method based on the sigmoid-energy conservation equation. BIOMEDICAL OPTICS EXPRESS 2021; 12:1792-1803. [PMID: 33996198 PMCID: PMC8086460 DOI: 10.1364/boe.417010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 02/10/2021] [Accepted: 02/16/2021] [Indexed: 06/12/2023]
Abstract
Optical coherence tomography (OCT) is an important medical diagnosis technology, but OCT images are inevitably interfered by speckle noise and other factors, which greatly reduce the quality of the OCT image. In order to improve the quality of the OCT image quickly, a fast OCT image enhancement method is proposed based on the fusion equation. The proposed method consists of three parts: edge detection, noise suppression, and image fusion. In this paper, the improved wave algorithm is used to detect the image edge and its fine features, and the averaging uncorrelated images method is used to suppress speckle noise and improve image contrast. In order to sharpen image edges while suppressing the speckle noise, a sigmoid-energy conservation equation (SE equation) is designed to fuse the edge detection image and the noise suppression image. The proposed method was tested on two publicly available datasets. Results show that the proposed method can effectively improve image contrast and sharpen image edges while suppressing the speckle noise. Compared with other state-of-the-art methods, the proposed method has better image enhancement effect and speed. Under the same or better enhancement effect, the processing speed of the proposed method is 2 ∼ 34 times faster than other methods.
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Affiliation(s)
- Shiliang Lou
- School of Precision Instruments and Opto-Electronic Engineering, Tianjin University, Tianjin 300072, China
| | - Xiaodong Chen
- School of Precision Instruments and Opto-Electronic Engineering, Tianjin University, Tianjin 300072, China
- Key Laboratory of Opto-Electronics Information Technology of Ministry of Education, Tianjin University, Tianjin 300072, China
| | - Jing Liu
- School of Precision Instruments and Opto-Electronic Engineering, Tianjin University, Tianjin 300072, China
| | - Yu Shi
- School of Precision Instruments and Opto-Electronic Engineering, Tianjin University, Tianjin 300072, China
| | - Hui Qu
- School of Precision Instruments and Opto-Electronic Engineering, Tianjin University, Tianjin 300072, China
| | - Yi Wang
- School of Precision Instruments and Opto-Electronic Engineering, Tianjin University, Tianjin 300072, China
- Key Laboratory of Opto-Electronics Information Technology of Ministry of Education, Tianjin University, Tianjin 300072, China
| | - Huaiyu Cai
- School of Precision Instruments and Opto-Electronic Engineering, Tianjin University, Tianjin 300072, China
- Key Laboratory of Opto-Electronics Information Technology of Ministry of Education, Tianjin University, Tianjin 300072, China
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Wang M, Zhu W, Yu K, Chen Z, Shi F, Zhou Y, Ma Y, Peng Y, Bao D, Feng S, Ye L, Xiang D, Chen X. Semi-Supervised Capsule cGAN for Speckle Noise Reduction in Retinal OCT Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:1168-1183. [PMID: 33395391 DOI: 10.1109/tmi.2020.3048975] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Speckle noise is the main cause of poor optical coherence tomography (OCT) image quality. Convolutional neural networks (CNNs) have shown remarkable performances for speckle noise reduction. However, speckle noise denoising still meets great challenges because the deep learning-based methods need a large amount of labeled data whose acquisition is time-consuming or expensive. Besides, many CNNs-based methods design complex structure based networks with lots of parameters to improve the denoising performance, which consume hardware resources severely and are prone to overfitting. To solve these problems, we propose a novel semi-supervised learning based method for speckle noise denoising in retinal OCT images. First, to improve the model's ability to capture complex and sparse features in OCT images, and avoid the problem of a great increase of parameters, a novel capsule conditional generative adversarial network (Caps-cGAN) with small number of parameters is proposed to construct the semi-supervised learning system. Then, to tackle the problem of retinal structure information loss in OCT images caused by lack of detailed guidance during unsupervised learning, a novel joint semi-supervised loss function composed of unsupervised loss and supervised loss is proposed to train the model. Compared with other state-of-the-art methods, the proposed semi-supervised method is suitable for retinal OCT images collected from different OCT devices and can achieve better performance even only using half of the training data.
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Daneshmand PG, Mehridehnavi A, Rabbani H. Reconstruction of Optical Coherence Tomography Images Using Mixed Low Rank Approximation and Second Order Tensor Based Total Variation Method. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:865-878. [PMID: 33232227 DOI: 10.1109/tmi.2020.3040270] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
This paper proposes a mixed low-rank approximation and second-order tensor-based total variation (LRSOTTV) approach for the super-resolution and denoising of retinal optical coherence tomography (OCT) images through effective utilization of nonlocal spatial correlations and local smoothness properties. OCT imaging relies on interferometry, which explains why OCT images suffer from a high level of noise. In addition, data subsampling is conducted during OCT A-scan and B-scan acquisition. Therefore, using effective super-resolution algorithms is necessary for reconstructing high-resolution clean OCT images. In this paper, a low-rank regularization approach is proposed for exploiting nonlocal self-similarity prior to OCT image reconstruction. To benefit from the advantages of the redundancy of multi-slice OCT data, we construct a third-order tensor by extracting the nonlocal similar three-dimensional blocks and grouping them by applying the k-nearest-neighbor method. Next, the nuclear norm is used as a regularization term to shrink the singular values of the constructed tensor in the non-local correlation direction. Further, the regularization approaches of the first-order tensor-based total variation (FOTTV) and SOTTV are proposed for better preservation of retinal layers and suppression of artifacts in OCT images. The alternative direction method of multipliers (ADMM) technique is then used to solve the resulting optimization problem. Our experiments show that integrating SOTTV instead of FOTTV into a low-rank approximation model can achieve noticeably improved results. Our experimental results on the denoising and super-resolution of OCT images demonstrate that the proposed model can provide images whose numerical and visual qualities are higher than those obtained by using state-of-the-art methods.
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Kande NA, Dakhane R, Dukkipati A, Yalavarthy PK. SiameseGAN: A Generative Model for Denoising of Spectral Domain Optical Coherence Tomography Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:180-192. [PMID: 32924938 DOI: 10.1109/tmi.2020.3024097] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Optical coherence tomography (OCT) is a standard diagnostic imaging method for assessment of ophthalmic diseases. The speckle noise present in the high-speed OCT images hampers its clinical utility, especially in Spectral-Domain Optical Coherence Tomography (SDOCT). In this work, a new deep generative model, called as SiameseGAN, for denoising Low signal-to-noise ratio (LSNR) B-scans of SDOCT has been developed. SiameseGAN is a Generative Adversarial Network (GAN) equipped with a siamese twin network. The siamese network module of the proposed SiameseGAN model helps the generator to generate denoised images that are closer to groundtruth images in the feature space, while the discriminator helps in making sure they are realistic images. This approach, unlike baseline dictionary learning technique (MSBTD), does not require an apriori high-quality image from the target imaging subject for denoising and takes less time for denoising. Moreover, various deep learning models that have been shown to be effective in performing denoising task in the SDOCT imaging were also deployed in this work. A qualitative and quantitative comparison on the performance of proposed method with these state-of-the-art denoising algorithms has been performed. The experimental results show that the speckle noise can be effectively mitigated using the proposed SiameseGAN along with faster denoising unlike existing approaches.
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Huang Z, Tang C, Xu M, Shen Y, Lei Z. Both speckle reduction and contrast enhancement for optical coherence tomography via sequential optimization in the logarithmic domain based on a refined Retinex model. APPLIED OPTICS 2020; 59:11087-11097. [PMID: 33361937 DOI: 10.1364/ao.405981] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Accepted: 11/19/2020] [Indexed: 06/12/2023]
Abstract
Optical coherence tomography (OCT) image enhancement is a challenging task because speckle reduction and contrast enhancement need to be addressed simultaneously and effectively. We present a refined Retinex model for guidance in improving the performance of enhancing OCT images accompanied by speckle noise; a physical explanation is provided. Based on this model, we establish two sequential optimization functions in the logarithmic domain for speckle reduction and contrast enhancement, respectively. More specifically, we obtain the despeckled image of an entire OCT image by solving the first optimization function. Incidentally, we can recover the speckle noise map through removing the despeckle component directly. Then, we estimate the illumination and reflectance by solving the second optimization function. Further, we apply the contrast-limited adaptive histogram equalization algorithm to adjust the illumination, and project it back to the reflectance for achieving contrast enhancement. Experimental results demonstrate the robustness and effectiveness of our proposed method. It performs well in both speckle reduction and contrast enhancement and is superior to the other two methods both in terms of qualitative analysis and quantitative assessment. Our method has the practical potential to improve the accuracy of manual screening and computer-aided diagnosis for retinal diseases.
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Xu M, Tang C, Hao F, Chen M, Lei Z. Texture preservation and speckle reduction in poor optical coherence tomography using the convolutional neural network. Med Image Anal 2020; 64:101727. [DOI: 10.1016/j.media.2020.101727] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 05/10/2020] [Accepted: 05/11/2020] [Indexed: 11/25/2022]
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Abstract
In recent years, convolutional neural networks (CNN) have been widely used in image denoising for their high performance. One difficulty in applying the CNN to medical image denoising such as speckle reduction in the optical coherence tomography (OCT) image is that a large amount of high-quality data is required for training, which is an inherent limitation for OCT despeckling. Recently, deep image prior (DIP) networks have been proposed for image restoration without pre-training since the CNN structures have the intrinsic ability to capture the low-level statistics of a single image. However, the DIP has difficulty finding a good balance between maintaining details and suppressing speckle noise. Inspired by DIP, in this paper, a sorted non-local statics which measures the signal autocorrelation in the differences between the constructed image and the input image is proposed for OCT image restoration. By adding the sorted non-local statics as a regularization loss in the DIP learning, more low-level image statistics are captured by CNN networks in the process of OCT image restoration. The experimental results demonstrate the superior performance of the proposed method over other state-of-the-art despeckling methods, in terms of objective metrics and visual quality.
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Daneshmand PG, Rabbani H, Mehridehnavi A. Super-Resolution of Optical Coherence Tomography Images by Scale Mixture Models. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2020; 29:5662-5676. [PMID: 32275595 DOI: 10.1109/tip.2020.2984896] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
In this paper, a new statistical model is proposed for the single image super-resolution of retinal Optical Coherence Tomography (OCT) images. OCT imaging relies on interfero-metry, which explains why OCT images suffer from a high level of noise. Moreover, data subsampling is carried out during the acquisition of OCT A-scans and B-scans. So, it is necessary to utilize effective super-resolution algorithms to reconstruct high-resolution clean OCT images. In this paper, a nonlocal sparse model-based Bayesian framework is proposed for OCT restoration. For this reason, by characterizing nonlocal patches with similar structures, known as a group, the sparse coefficients of each group of OCT images are modeled by the scale mixture models. In this base, the coefficient vector is decomposed into the point-wise product of a random vector and a positive scaling variable. Estimation of the sparse coefficients depends on the proposed distribution for the random vector and scaling variable where the Laplacian random vector and Generalized Extreme-Value (GEV) scale parameter (Laplacian+GEV model) show the best goodness of fit for each group of OCT images. Finally, a new OCT super-resolution method based on this new scale mixture model is introduced, where the maximum a posterior estimation of both sparse coefficients and scaling variables are calculated efficiently by applying an alternating minimization method. Our experimental results prove that the proposed OCT super-resolution method based on the Laplacian+GEV model outperforms other competing methods in terms of both subjective and objective visual qualities.
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Esmaeili M, Dehnavi AM, Hajizadeh F, Rabbani H. Three-dimensional curvelet-based dictionary learning for speckle noise removal of optical coherence tomography. BIOMEDICAL OPTICS EXPRESS 2020; 11:586-608. [PMID: 32133216 PMCID: PMC7041443 DOI: 10.1364/boe.377021] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 12/07/2019] [Accepted: 12/07/2019] [Indexed: 05/27/2023]
Abstract
Optical coherence tomography (OCT) is a recently emerging non-invasive diagnostic tool useful in several medical applications such as ophthalmology, cardiology, gastroenterology and dermatology. One of the major problems with OCT pertains to its low contrast due to the presence of multiplicative speckle noise, which limits the signal-to-noise ratio (SNR) and obscures low-intensity and small features. In this paper, we recommend a new method using the 3D curvelet based K-times singular value decomposition (K-SVD) algorithm for speckle noise reduction and contrast enhancement of the intra-retinal layers of 3D Spectral-Domain OCT (3D-SDOCT) images. In order to benefit from the near-optimum properties of curvelet transform (such as good directional selectivity) on top of dictionary learning, we propose a new plan in dictionary learning by using the curvelet atoms as the initial dictionary. For this reason, the curvelet transform of the noisy image is taken and then the noisy coefficients matrix in each scale, rotation and spatial coordinates is passed through the K-SVD denoising algorithm with predefined 3D initial dictionary that is adaptively selected from thresholded coefficients in the same subband of the image. During the denoising of curvelet coefficients, we can also modify them for the purpose of contrast enhancement of intra-retinal layers. We demonstrate the ability of our proposed algorithm in the speckle noise reduction of 17 publicly available 3D OCT data sets, each of which contains 100 B-scans of size 512×1000 with and without neovascular age-related macular degeneration (AMD) images acquired using SDOCT, Bioptigen imaging systems. Experimental results show that an improvement from 1.27 to 7.81 in contrast to noise ratio (CNR), and from 38.09 to 1983.07 in equivalent number of looks (ENL) is achieved, which would outperform existing state-of-the-art OCT despeckling methods.
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Affiliation(s)
- Mahad Esmaeili
- Department of Bioelectrics and Biomedical
Engineering, Medical Image & Signal Processing Research Center,
School of Advanced Technologies in Medicine, Isfahan University of
Medical Sciences, Isfahan, Iran
- Department of Medical Bioengineering,
Faculty of Advanced Medical Sciences, Tabriz University of Medical
Sciences, Tabriz, Iran
| | - Alireza Mehri Dehnavi
- Department of Bioelectrics and Biomedical
Engineering, Medical Image & Signal Processing Research Center,
School of Advanced Technologies in Medicine, Isfahan University of
Medical Sciences, Isfahan, Iran
| | - Fedra Hajizadeh
- Noor Ophthalmology Research Center, Noor
Eye Hospital, Tehran, Iran
| | - Hosseini Rabbani
- Department of Bioelectrics and Biomedical
Engineering, Medical Image & Signal Processing Research Center,
School of Advanced Technologies in Medicine, Isfahan University of
Medical Sciences, Isfahan, Iran
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Hu Y, Tang C, Xu M, Lei Z. Selective retinex enhancement based on the clustering algorithm and block-matching 3D for optical coherence tomography images. APPLIED OPTICS 2019; 58:9861-9869. [PMID: 31873631 DOI: 10.1364/ao.58.009861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Accepted: 11/07/2019] [Indexed: 06/10/2023]
Abstract
It is important to enhance the contrast and remove the speckle noise for optical coherence tomography (OCT) images. In this paper, we propose a selective retinex enhancement method based on the fuzzy c-means (FCM) clustering algorithm to enhance only the structure part in OCT images and combines with the block-matching 3D (BM3D) algorithm for filtering. In the proposed selective retinex enhancement method, we first calculate the feature image of the original image, which includes the mean value and standard deviation of each pixel in the original image and its correlation image. Second, by applying the FCM clustering algorithm to the feature image, a mask is generated that can distinguish the structure part from the background part in the OCT image. Then, the mask is applied to the multi-scale retinex algorithm, and only the structure part in the OCT image is enhanced. Moreover, the BM3D method is applied to filter the enhanced image. Experimental results demonstrate that the proposed method performs impressively in improving the contrast and removing the speckle noise of OCT images, and it provides better quantitative performance in terms of signal-to-noise ratio, contrast-to-noise ratio, equivalent number of looks, and the edge preservation parameter $ \beta $β.
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Ren R, Guo Z, Jia Z, Yang J, Kasabov NK, Li C. Speckle Noise Removal in Image-based Detection of Refractive Index Changes in Porous Silicon Microarrays. Sci Rep 2019; 9:15001. [PMID: 31628389 PMCID: PMC6802097 DOI: 10.1038/s41598-019-51435-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2018] [Accepted: 08/30/2019] [Indexed: 11/19/2022] Open
Abstract
Based on porous silicon (PSi) microarray images, we propose a new method called the phagocytosis algorithm (PGY) for removing the influence of speckle noise on image gray values. In a theoretical analysis, speckle noise of different intensities is added to images, and a suitable denoising method is developed to restore the image gray level. This method can be used to reduce the influence of speckle noise on the gray values of PSi microarray images to improve the accuracy of detection and increase detection sensitivity. In experiments, the method is applied to detect refractive index changes in PSi microcavity images, and a good linear relationship between the gray level change and the refractive index change is obtained. In addition, the algorithm is applied to a PSi microarray image, and good results are obtained.
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Affiliation(s)
- Ruyong Ren
- College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China
| | - Zhiqing Guo
- College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China
| | - Zhenhong Jia
- College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China.
| | - Jie Yang
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Nikola K Kasabov
- Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland, 1020, New Zealand
| | - Chuanxi Li
- School of Physical Science and Technology, Xinjiang University, Urumqi, 830046, China
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Shi F, Cai N, Gu Y, Hu D, Ma Y, Chen Y, Chen X. DeSpecNet: a CNN-based method for speckle reduction in retinal optical coherence tomography images. Phys Med Biol 2019; 64:175010. [PMID: 31342925 DOI: 10.1088/1361-6560/ab3556] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Speckle is a major quality degrading factor in optical coherence tomography (OCT) images. In this work we propose a new deep learning network for speckle reduction in retinal OCT images, termed DeSpecNet. Unlike traditional algorithms, the model can learn from training data instead of manually selecting parameters such as noise level. The proposed deep convolutional neural network (CNN) applies strategies including residual learning, shortcut connection, batch normalization and leaky rectified linear units to achieve good despeckling performance. Application of the proposed method to the OCT images shows great improvement in both visual quality and quantitative indices. The proposed method provides good generalization ability for different types of retinal OCT images. It outperforms state-of-the-art methods in suppressing speckles and revealing subtle features while preserving edges.
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Affiliation(s)
- Fei Shi
- School of Electronics and Information Engineering, Soochow University, Suzhou, People's Republic of China. These authors contributed equally
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Applying Speckle Noise Suppression to Refractive Indices Change Detection in Porous Silicon Microarrays. SENSORS 2019; 19:s19132975. [PMID: 31284494 PMCID: PMC6651720 DOI: 10.3390/s19132975] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Revised: 06/19/2019] [Accepted: 06/29/2019] [Indexed: 01/31/2023]
Abstract
The gray value method can be used to detect gray value changes of each unit almost parallel to the surface image of PSi (porous silicon) microarrays and indirectly measure the refractive index changes of each unit. However, the speckles of different noise intensities produced by lasers on a porous silicon surface have different effects on the gray value of the measured image. This results in inaccurate results of refractive index changes obtained from the change in gray value. Therefore, it is very important to reduce the influence of speckle noise on measurement results. In this paper, a new algorithm based on the concepts of probability-based nonlocal-means filtering (PNLM), gradient operator, and median filtering is proposed for gray value restoration of porous silicon microarray images. A good linear relationship between gray value change and refractive index change is obtained, which can reduce the influence of speckle noise on the gray value of the PSi microarray image, improving detection accuracy. This means the method based on gray value change detection can be applied to the biological detection of PSi microarray arrays.
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Huang Y, Lu Z, Shao Z, Ran M, Zhou J, Fang L, Zhang Y. Simultaneous denoising and super-resolution of optical coherence tomography images based on generative adversarial network. OPTICS EXPRESS 2019; 27:12289-12307. [PMID: 31052772 DOI: 10.1364/oe.27.012289] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
Optical coherence tomography (OCT) has become a very promising diagnostic method in clinical practice, especially for ophthalmic diseases. However, speckle noise and low sampling rates have intensively reduced the quality of OCT images, which prevents the development of OCT-assisted diagnosis. Therefore, we propose a generative adversarial network-based approach (named SDSR-OCT) to simultaneously denoise and super-resolve OCT images. Moreover, we trained three different super-resolution models with different upscale factors (2× , 4× and 8×) to adapt to the corresponding downsampling rates. We also quantitatively and qualitatively compared our proposed method with some well-known algorithms. The experimental results show that our approach can effectively suppress speckle noise and can super-resolve OCT images at different scales.
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Ma Y, Chen X, Zhu W, Cheng X, Xiang D, Shi F. Speckle noise reduction in optical coherence tomography images based on edge-sensitive cGAN. BIOMEDICAL OPTICS EXPRESS 2018; 9:5129-5146. [PMID: 30460118 PMCID: PMC6238896 DOI: 10.1364/boe.9.005129] [Citation(s) in RCA: 79] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Revised: 09/07/2018] [Accepted: 09/16/2018] [Indexed: 05/18/2023]
Abstract
Speckle noise in optical coherence tomography (OCT) impairs both the visual quality and the performance of automatic analysis. Edge preservation is an important issue for speckle reduction. In this paper, we propose an end-to-end framework for simultaneous speckle reduction and contrast enhancement for retinal OCT images based on the conditional generative adversarial network (cGAN). The edge loss function is added to the final objective so that the model is sensitive to the edge-related details. We also propose a novel method for obtaining clean images for training from outputs of commercial OCT scanners. The results show that the overall denoising performance of the proposed method is better than other traditional methods and deep learning methods. The proposed model also has good generalization ability and is capable of despeckling different types of retinal OCT images.
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Affiliation(s)
- Yuhui Ma
- School of Electronic and Information Engineering, Soochow University, Suzhou 215006, China
- contributed equally
| | - Xinjian Chen
- School of Electronic and Information Engineering, Soochow University, Suzhou 215006, China
- State Key Laboratory of Radiation Medicine and Protection, Soochow University, Suzhou 215123, China
- contributed equally
| | - Weifang Zhu
- School of Electronic and Information Engineering, Soochow University, Suzhou 215006, China
- State Key Laboratory of Radiation Medicine and Protection, Soochow University, Suzhou 215123, China
- Collaborative Innovation Center of IoT Technology and Intelligent Systems, Minjiang University, Fuzhou 350108, China
| | - Xuena Cheng
- School of Electronic and Information Engineering, Soochow University, Suzhou 215006, China
| | - Dehui Xiang
- School of Electronic and Information Engineering, Soochow University, Suzhou 215006, China
- State Key Laboratory of Radiation Medicine and Protection, Soochow University, Suzhou 215123, China
| | - Fei Shi
- School of Electronic and Information Engineering, Soochow University, Suzhou 215006, China
- State Key Laboratory of Radiation Medicine and Protection, Soochow University, Suzhou 215123, China
- Collaborative Innovation Center of IoT Technology and Intelligent Systems, Minjiang University, Fuzhou 350108, China
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PCANet based nonlocal means method for speckle noise removal in ultrasound images. PLoS One 2018; 13:e0205390. [PMID: 30312331 PMCID: PMC6185735 DOI: 10.1371/journal.pone.0205390] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2017] [Accepted: 09/21/2018] [Indexed: 12/02/2022] Open
Abstract
Speckle reduction remains a critical issue for ultrasound image processing and analysis. The nonlocal means (NLM) filter has recently attached much attention due to its competitive despeckling performance. However, the existing NLM methods usually determine the similarity between two patches by directly utilizing the gray-level information of the noisy image, which renders it difficult to represent the structural similarity of ultrasound images effectively. To address this problem, the NLM method based on the simple deep learning baseline named PCANet is proposed by introducing the intrinsic features of image patches extracted by this network rather than the pixel intensities into the pixel similarity computation. In this approach, the improved two-stage PCANet is proposed by using Parametric Rectified Linear Unit (PReLU) activation function instead of the binary hashing and block histograms in the original PCANet. This model is firstly trained on the ultrasound database to learn the convolution kernels. Then, the trained PCANet is utilized to extract the intrinsic features from the image patches in the pre-denoised version of the noisy image to be despeckled. These obtained features are concatenated together to determine the structural similarity between image patches in the NLM method, based on which the weighted mean of all pixels in a search window is computed to produce the final despeckled image. Extensive experiments have been conducted on a variety of images to demonstrate the superiority of the proposed method over several well-known despeckling algorithm and the PCANet based NLM method using ReLU function and sigmoid function. Visual inspection indicates that the proposed method outperforms the compared methods in reducing speckle noise and preserving image details. The quantitative comparisons show that among all the evaluated methods, our method produces the best structural similarity index metrics (SSIM) values for the synthetic image, as well as the highest equivalent number of looks (ENL) value for the simulated image and the clinical ultrasound images.
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Leedumrongwatthanakun S, Thavarungkul P, Kanatharana P, Buranachai C. Wavelet analysis on time-frequency plane of optical coherence tomography: simultaneous signal quality improvement in structural and velocity images. OPTICS LETTERS 2018; 43:3730-3733. [PMID: 30067666 DOI: 10.1364/ol.43.003730] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2018] [Accepted: 07/09/2018] [Indexed: 06/08/2023]
Abstract
In this Letter, we utilize one-dimensional wavelet analysis to improve the quality of morphology images and velocity profiles of optical coherence tomography simultaneously, by performing analysis on the complex time-frequency plane of raw interferograms, prior to image construction. The results indicate a robust signal improvement that also preserves accuracy for both morphology and velocity information and has been demonstrated on a variety of samples with diverse flow speeds and morphologies.
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LIU XUAN, ZAKI FARZANA, RENAUD DYLAN. Assessment and removal of additive noise in a complex optical coherence tomography signal based on Doppler variation analysis. APPLIED OPTICS 2018; 57:2873-2880. [PMID: 29714288 PMCID: PMC6036923 DOI: 10.1364/ao.57.002873] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2018] [Accepted: 03/12/2018] [Indexed: 05/30/2023]
Abstract
In this study, we investigate and validate a novel approach to assess and remove additive noise for optical coherence tomography (OCT) imaging. Our method first generates a map of additive noise for the OCT image through Doppler variation analysis. We then remove the additive noise from the real and imaginary parts of the complex OCT signal through pixelwise Wiener filtering. Our results show that the method described in this manuscript improves the sensitivity of OCT imaging and preserves the spatial resolution without the need to modify the imaging apparatus and data acquisition protocol.
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Affiliation(s)
- XUAN LIU
- Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102
| | - FARZANA ZAKI
- Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102
| | - DYLAN RENAUD
- Department of Physics, New Jersey Institute of Technology, Newark, NJ 07102
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