1
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Han M, Dang Y, Han J. Denoising and Baseline Correction Methods for Raman Spectroscopy Based on Convolutional Autoencoder: A Unified Solution. SENSORS (BASEL, SWITZERLAND) 2024; 24:3161. [PMID: 38794016 PMCID: PMC11125329 DOI: 10.3390/s24103161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 05/07/2024] [Accepted: 05/10/2024] [Indexed: 05/26/2024]
Abstract
Preprocessing plays a key role in Raman spectral analysis. However, classical preprocessing algorithms often have issues with reducing Raman peak intensities and changing the peak shape when processing spectra. This paper introduces a unified solution for preprocessing based on a convolutional autoencoder to enhance Raman spectroscopy data. One is a denoising algorithm that uses a convolutional denoising autoencoder (CDAE model), and the other is a baseline correction algorithm based on a convolutional autoencoder (CAE+ model). The CDAE model incorporates two additional convolutional layers in its bottleneck layer for enhanced noise reduction. The CAE+ model not only adds convolutional layers at the bottleneck but also includes a comparison function after the decoding for effective baseline correction. The proposed models were validated using both simulated spectra and experimental spectra measured with a Raman spectrometer system. Comparing their performance with that of traditional signal processing techniques, the results of the CDAE-CAE+ model show improvements in noise reduction and Raman peak preservation.
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Affiliation(s)
- Ming Han
- Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tianjin 300350, China; (M.H.); (J.H.)
- Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, Nankai University, Tianjin 300350, China
- Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin 300350, China
- Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Shenzhen 518083, China
| | - Yu Dang
- Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tianjin 300350, China; (M.H.); (J.H.)
- Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, Nankai University, Tianjin 300350, China
- Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin 300350, China
- Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Shenzhen 518083, China
| | - Jianda Han
- Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tianjin 300350, China; (M.H.); (J.H.)
- Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, Nankai University, Tianjin 300350, China
- Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin 300350, China
- Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Shenzhen 518083, China
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2
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Tiantian W, Hu Z, Guan Y. An efficient lightweight network for image denoising using progressive residual and convolutional attention feature fusion. Sci Rep 2024; 14:9554. [PMID: 38664440 PMCID: PMC11045760 DOI: 10.1038/s41598-024-60139-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 04/19/2024] [Indexed: 04/28/2024] Open
Abstract
While deep learning has become the go-to method for image denoising due to its impressive noise removal capabilities, excessive network depth often plagues existing approaches, leading to significant computational burdens. To address this critical bottleneck, we propose a novel lightweight progressive residual and attention mechanism fusion network that effectively alleviates these limitations. This architecture tackles both Gaussian and real-world image noise with exceptional efficacy. Initiated through dense blocks (DB) tasked with discerning the noise distribution, this approach substantially reduces network parameters while comprehensively extracting local image features. The network then adopts a progressive strategy, whereby shallow convolutional features are incrementally integrated with deeper features, establishing a residual fusion framework adept at extracting encompassing global features relevant to noise characteristics. The process concludes by integrating the output feature maps from each DB and the robust edge features from the convolutional attention feature fusion module (CAFFM). These combined elements are then directed to the reconstruction layer, ultimately producing the final denoised image. Empirical analyses conducted in environments characterized by Gaussian white noise and natural noise, spanning noise levels 15-50, indicate a marked enhancement in performance. This assertion is quantitatively corroborated by increased average values in metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index for Color images (FSIMc), outperforming the outcomes of more than 20 existing methods across six varied datasets. Collectively, the network delineated in this research exhibits exceptional adeptness in image denoising. Simultaneously, it adeptly preserves essential image features such as edges and textures, thereby signifying a notable progression in the domain of image processing. The proposed model finds applicability in a range of image-centric domains, encompassing image processing, computer vision, video analysis, and pattern recognition.
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Affiliation(s)
- Wang Tiantian
- School of Computer and Software Engineering, Xias University, Zhengzhou, 451150, Henan, China
| | - Zhihua Hu
- School of Computer, Huanggang Normal University, Huanggang, 438000, Hubei, China.
| | - Yurong Guan
- School of Computer, Huanggang Normal University, Huanggang, 438000, Hubei, China.
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3
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Zhang J, Gong W, Ye L, Wang F, Shangguan Z, Cheng Y. A Review of deep learning methods for denoising of medical low-dose CT images. Comput Biol Med 2024; 171:108112. [PMID: 38387380 DOI: 10.1016/j.compbiomed.2024.108112] [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: 10/19/2023] [Revised: 01/18/2024] [Accepted: 02/04/2024] [Indexed: 02/24/2024]
Abstract
To prevent patients from being exposed to excess of radiation in CT imaging, the most common solution is to decrease the radiation dose by reducing the X-ray, and thus the quality of the resulting low-dose CT images (LDCT) is degraded, as evidenced by more noise and streaking artifacts. Therefore, it is important to maintain high quality CT image while effectively reducing radiation dose. In recent years, with the rapid development of deep learning technology, deep learning-based LDCT denoising methods have become quite popular because of their data-driven and high-performance features to achieve excellent denoising results. However, to our knowledge, no relevant article has so far comprehensively introduced and reviewed advanced deep learning denoising methods such as Transformer structures in LDCT denoising tasks. Therefore, based on the literatures related to LDCT image denoising published from year 2016-2023, and in particular from 2020 to 2023, this study presents a systematic survey of current situation, and challenges and future research directions in LDCT image denoising field. Four types of denoising networks are classified according to the network structure: CNN-based, Encoder-Decoder-based, GAN-based, and Transformer-based denoising networks, and each type of denoising network is described and summarized from the perspectives of structural features and denoising performances. Representative deep-learning denoising methods for LDCT are experimentally compared and analyzed. The study results show that CNN-based denoising methods capture image details efficiently through multi-level convolution operation, demonstrating superior denoising effects and adaptivity. Encoder-decoder networks with MSE loss, achieve outstanding results in objective metrics. GANs based methods, employing innovative generators and discriminators, obtain denoised images that exhibit perceptually a closeness to NDCT. Transformer-based methods have potential for improving denoising performances due to their powerful capability in capturing global information. Challenges and opportunities for deep learning based LDCT denoising are analyzed, and future directions are also presented.
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Affiliation(s)
- Ju Zhang
- College of Information Science and Technology, Hangzhou Normal University, Hangzhou, China.
| | - Weiwei Gong
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China.
| | - Lieli Ye
- College of Information Science and Technology, Hangzhou Normal University, Hangzhou, China.
| | - Fanghong Wang
- Zhijiang College, Zhejiang University of Technology, Shaoxing, China.
| | - Zhibo Shangguan
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China.
| | - Yun Cheng
- Department of Medical Imaging, Zhejiang Hospital, Hangzhou, China.
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4
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Uz M, Akyılmaz O, Shum CK, Atman KG, Olgun S, Güneş Ö. High-resolution temporal gravity field data products: Monthly mass grids and spherical harmonics from 1994 to 2021. Sci Data 2024; 11:71. [PMID: 38218975 PMCID: PMC10787793 DOI: 10.1038/s41597-023-02887-5] [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: 05/16/2023] [Accepted: 12/27/2023] [Indexed: 01/15/2024] Open
Abstract
Since April 2002, Gravity Recovery and Climate Experiment (GRACE) and GRACE-FO (FollowOn) satellite gravimetry missions have provided precious data for monitoring mass variations within the hydrosphere, cryosphere, and oceans with unprecedented accuracy and resolution. However, the long-term products of mass variations prior to GRACE-era may allow for a better understanding of spatio-temporal changes in climate-induced geophysical phenomena, e.g., terrestrial water cycle, ice sheet and glacier mass balance, sea level change and ocean bottom pressure (OBP). Here, climate-driven mass anomalies are simulated globally at 1.0° × 1.0° spatial and monthly temporal resolutions from January 1994 to January 2021 using an in-house developed hybrid Deep Learning architecture considering GRACE/-FO mascon and SLR-inferred gravimetry, ECMWF Reanalysis-5 data, and normalized time tag information as training datasets. Internally, we consider mathematical metrics such as RMSE, NSE and comparisons to previous studies, and externally, we compare our simulations to GRACE-independent datasets such as El-Nino and La-Nina indexes, Global Mean Sea Level, Earth Orientation Parameters-derived low-degree spherical harmonic coefficients, and in-situ OBP measurements for validation.
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Affiliation(s)
- Metehan Uz
- Dept. of Geomatics Eng., Istanbul Technical University, Istanbul, Turkey
| | - Orhan Akyılmaz
- Dept. of Geomatics Eng., Istanbul Technical University, Istanbul, Turkey.
| | - C K Shum
- Division of Geodetic Science, School of Earth Sciences, The Ohio State University, Columbus, Ohio, USA
| | - Kazım Gökhan Atman
- School of Mathematical Sciences, Queen Mary University of London, London, England
- Department of Physics, Ege University, Izmir, Turkey
| | - Sevda Olgun
- Dept. of Geomatics Eng., Kocaeli University, Kocaeli, Turkey
| | - Özge Güneş
- Dept. of Geomatics Eng., Yıldız Technical University, Istanbul, Turkey
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5
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Zhang X. Image denoising and segmentation model construction based on IWOA-PCNN. Sci Rep 2023; 13:19848. [PMID: 37963960 PMCID: PMC10645996 DOI: 10.1038/s41598-023-47089-6] [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: 09/15/2023] [Accepted: 11/08/2023] [Indexed: 11/16/2023] Open
Abstract
The research suggests a method to improve the present pulse coupled neural network (PCNN), which has a complex structure and unsatisfactory performance in image denoising and image segmentation. Then, a multi strategy collaborative improvement whale optimization algorithm (WOA) is proposed, and an improved whale optimization algorithm (IWOA) is constructed. IWOA is used to find the optimal parameter values of PCNN to optimize PCNN. By combining the aforementioned components, the IWOA-PCNN model had the best image denoising performance, and the produced images were crisper and preserve more information. IWOA-PCNN processed pictures have an average PSNR of 35.87 and an average MSE of 0.24. The average processing time for photos with noise is typically 24.80 s, which is 7.30 s and 7.76 s faster than the WTGAN and IGA-NLM models, respectively. Additionally, the average NU value measures 0.947, and the average D value exceeds 1000. The aforementioned findings demonstrate that the suggested method can successfully enhance the PCNN, improving its capability for image denoising and image segmentation. This can, in part, encourage the use and advancement of the PCNN.
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Affiliation(s)
- Xiaojun Zhang
- College of Software Technology, Henan Finance University, Zhengzhou, 450000, China.
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6
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Chen W, Li B. Overcoming Periodic Stripe Noise in Infrared Linear Array Images: The Fourier-Assisted Correlative Denoising Method. SENSORS (BASEL, SWITZERLAND) 2023; 23:8716. [PMID: 37960416 PMCID: PMC10650797 DOI: 10.3390/s23218716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 10/09/2023] [Accepted: 10/23/2023] [Indexed: 11/15/2023]
Abstract
Infrared linear array detectors frequently experience vertical, low-frequency, and periodic stripe noise during imaging, stemming from electro-mechanical interference. Unlike conventional periodic disturbances, this interference showcases long periodicities and is uniquely columnar in orientation. Its presence, especially within the low-frequency domain, renders conventional filtering techniques ineffective and, at times, detrimental to image quality. Addressing this challenge, we introduce Fourier-Assisted Correlative Denoising (FACD), a correlation-centric denoising approach tailored for such unique interference patterns. This mechanism begins with the capture of a pure background image, inclusive of periodic noise, during the non-uniform correction phase of the infrared detector. Leveraging the noise's frequency domain attributes, we extract a one-dimensional single-cycle noise signal. The infrared image is subsequently segmented into parts, and using the detected noise periodicity, the one-dimensional signals for each segment are computed. By leveraging the correlation between these signals and the benchmark one-dimensional noise pattern, we ascertain the noise profile within each segment. This profile is then employed for spatial domain denoising across the entire image frame. Empirical assessments confirm that the FACD outperforms contemporary denoising techniques by augmenting the peak signal-to-noise ratio by approximately 2.5 dB, underscoring its superior robustness. Furthermore, in light of its specificity to this noise model, FACD rapidly denoises high-resolution real infrared linear array scans, thus meeting the stringent real-time and resolution imperatives of advanced infrared linear array scanning apparatuses.
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Affiliation(s)
- Weicong Chen
- Key Laboratory of Infrared System Detection and Imaging Technology, Chinese Academy of Sciences, Shanghai 200083, China;
- Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Bohan Li
- Key Laboratory of Infrared System Detection and Imaging Technology, Chinese Academy of Sciences, Shanghai 200083, China;
- Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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7
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Sun L, Lin L, Yao X, Zhang Y, Tao Z, Ling P. Real-Time Recognition Method for Key Signals of Rock Fracture Acoustic Emissions Based on Deep Learning. SENSORS (BASEL, SWITZERLAND) 2023; 23:8513. [PMID: 37896608 PMCID: PMC10610656 DOI: 10.3390/s23208513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 10/10/2023] [Accepted: 10/11/2023] [Indexed: 10/29/2023]
Abstract
The characteristics of acoustic emission signals generated in the process of rock deformation and fission contain rich information on internal rock damage. The use of acoustic emissions monitoring technology can analyze and identify the precursor information of rock failure. At present, in the field of acoustic emissions monitoring and the early warning of rock fracture disasters, there is no real-time identification method for a disaster precursor characteristic signal. It is easy to lose information by analyzing the characteristic parameters of traditional acoustic emissions to find signals that serve as precursors to disasters, and analysis has mostly been based on post-analysis, which leads to poor real-time recognition of disaster precursor characteristics and low application levels in the engineering field. Based on this, this paper regards the acoustic emissions signal of rock fracture as a kind of speech signal generated by rock fracture uses this idea of speech recognition for reference alongside spectral analysis (STFT) and Mel frequency analysis to realize the feature extraction of acoustic emissions from rock fracture. In deep learning, based on the VGG16 convolutional neural network and AlexNet convolutional neural network, six intelligent real-time recognition models of rock fracture and key acoustic emission signals were constructed, and the network structure and loss function of traditional VGG16 were optimized. The experimental results show that these six deep-learning models can achieve the real-time intelligent recognition of key signals, and Mel, combined with the improved VGG16, achieved the best performance with 87.68% accuracy and 81.05% recall. Then, by comparing multiple groups of signal recognition models, Mel+VGG-FL proposed in this paper was verified as having a high recognition accuracy and certain recognition efficiency, performing the intelligent real-time recognition of key acoustic emission signals in the process of rock fracture more accurately, which can provide new ideas and methods for related research and the real-time intelligent recognition of rock fracture precursor characteristics.
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Affiliation(s)
- Lin Sun
- Hebei Green Intelligent Mining Technology Innovation Center, Tangshan 063210, China; (L.S.); (L.L.); (Y.Z.); (P.L.)
- College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, China
| | - Lisen Lin
- Hebei Green Intelligent Mining Technology Innovation Center, Tangshan 063210, China; (L.S.); (L.L.); (Y.Z.); (P.L.)
- College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, China
| | - Xulong Yao
- Hebei Green Intelligent Mining Technology Innovation Center, Tangshan 063210, China; (L.S.); (L.L.); (Y.Z.); (P.L.)
- College of Mining Engineering, North China University of Science and Technology, Tangshan 063210, China
| | - Yanbo Zhang
- Hebei Green Intelligent Mining Technology Innovation Center, Tangshan 063210, China; (L.S.); (L.L.); (Y.Z.); (P.L.)
- College of Mining Engineering, North China University of Science and Technology, Tangshan 063210, China
| | - Zhigang Tao
- School of Mechanical and Architectural Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China;
- State Key Laboratory for Geomechanics and Deep Underground Engineering, Beijing 100083, China
| | - Peng Ling
- Hebei Green Intelligent Mining Technology Innovation Center, Tangshan 063210, China; (L.S.); (L.L.); (Y.Z.); (P.L.)
- College of Mining Engineering, North China University of Science and Technology, Tangshan 063210, China
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8
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Mehta AS, Teymoori S, Recendez C, Fregoso D, Gallegos A, Yang HY, Aslankoohi E, Rolandi M, Isseroff RR, Zhao M, Gomez M. Quantifying innervation facilitated by deep learning in wound healing. Sci Rep 2023; 13:16885. [PMID: 37803028 PMCID: PMC10558471 DOI: 10.1038/s41598-023-42743-5] [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: 06/20/2023] [Accepted: 09/14/2023] [Indexed: 10/08/2023] Open
Abstract
The peripheral nerves (PNs) innervate the dermis and epidermis, and are suggested to play an important role in wound healing. Several methods to quantify skin innervation during wound healing have been reported. Those usually require multiple observers, are complex and labor-intensive, and the noise/background associated with the immunohistochemistry (IHC) images could cause quantification errors/user bias. In this study, we employed the state-of-the-art deep neural network, Denoising Convolutional Neural Network (DnCNN), to perform pre-processing and effectively reduce the noise in the IHC images. Additionally, we utilized an automated image analysis tool, assisted by Matlab, to accurately determine the extent of skin innervation during various stages of wound healing. The 8 mm wound is generated using a circular biopsy punch in the wild-type mouse. Skin samples were collected on days 3, 7, 10 and 15, and sections from paraffin-embedded tissues were stained against pan-neuronal marker- protein-gene-product 9.5 (PGP 9.5) antibody. On day 3 and day 7, negligible nerve fibers were present throughout the wound with few only on the lateral boundaries of the wound. On day 10, a slight increase in nerve fiber density appeared, which significantly increased on day 15. Importantly, we found a positive correlation (R2 = 0.926) between nerve fiber density and re-epithelization, suggesting an association between re-innervation and re-epithelization. These results established a quantitative time course of re-innervation in wound healing, and the automated image analysis method offers a novel and useful tool to facilitate the quantification of innervation in the skin and other tissues.
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Affiliation(s)
- Abijeet Singh Mehta
- Department of Dermatology, University of California, Davis, CA, 95616, USA.
- Department of Ophthalmology, University of California, Davis, CA, 95616, USA.
| | - Sam Teymoori
- Department of Applied Mathematics, University of California, Santa Cruz, CA, 95064, USA
| | - Cynthia Recendez
- Department of Dermatology, University of California, Davis, CA, 95616, USA
- Department of Ophthalmology, University of California, Davis, CA, 95616, USA
| | - Daniel Fregoso
- Department of Dermatology, University of California, Davis, CA, 95616, USA
| | - Anthony Gallegos
- Department of Dermatology, University of California, Davis, CA, 95616, USA
| | - Hsin-Ya Yang
- Department of Dermatology, University of California, Davis, CA, 95616, USA
| | - Elham Aslankoohi
- Department of Electrical and Computer Engineering, University of California, Santa Cruz, CA, 95064, USA
| | - Marco Rolandi
- Department of Electrical and Computer Engineering, University of California, Santa Cruz, CA, 95064, USA
| | | | - Min Zhao
- Department of Dermatology, University of California, Davis, CA, 95616, USA.
- Department of Ophthalmology, University of California, Davis, CA, 95616, USA.
| | - Marcella Gomez
- Department of Applied Mathematics, University of California, Santa Cruz, CA, 95064, USA.
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9
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Hartbauer M. A Simple Denoising Algorithm for Real-World Noisy Camera Images. J Imaging 2023; 9:185. [PMID: 37754949 PMCID: PMC10532776 DOI: 10.3390/jimaging9090185] [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: 08/04/2023] [Revised: 09/12/2023] [Accepted: 09/15/2023] [Indexed: 09/28/2023] Open
Abstract
The noise statistics of real-world camera images are challenging for any denoising algorithm. Here, I describe a modified version of a bionic algorithm that improves the quality of real-word noisy camera images from a publicly available image dataset. In the first step, an adaptive local averaging filter was executed for each pixel to remove moderate sensor noise while preserving fine image details and object contours. In the second step, image sharpness was enhanced by means of an unsharp mask filter to generate output images that are close to ground-truth images (multiple averages of static camera images). The performance of this denoising algorithm was compared with five popular denoising methods: bm3d, wavelet, non-local means (NL-means), total variation (TV) denoising and bilateral filter. Results show that the two-step filter had a performance that was similar to NL-means and TV filtering. Bm3d had the best denoising performance but sometimes led to blurry images. This novel two-step filter only depends on a single parameter that can be obtained from global image statistics. To reduce computation time, denoising was restricted to the Y channel of YUV-transformed images and four image segments were simultaneously processed in parallel on a multi-core processor.
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10
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Zharov Y, Ametova E, Spiecker R, Baumbach T, Burca G, Heuveline V. Shot noise reduction in radiographic and tomographic multi-channel imaging with self-supervised deep learning. OPTICS EXPRESS 2023; 31:26226-26244. [PMID: 37710488 DOI: 10.1364/oe.492221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 06/13/2023] [Indexed: 09/16/2023]
Abstract
Shot noise is a critical issue in radiographic and tomographic imaging, especially when additional constraints lead to a significant reduction of the signal-to-noise ratio. This paper presents a method for improving the quality of noisy multi-channel imaging datasets, such as data from time or energy-resolved imaging, by exploiting structural similarities between channels. To achieve that, we broaden the application domain of the Noise2Noise self-supervised denoising approach. The method draws pairs of samples from a data distribution with identical signals but uncorrelated noise. It is applicable to multi-channel datasets if adjacent channels provide images with similar enough information but independent noise. We demonstrate the applicability and performance of the method via three case studies, namely spectroscopic X-ray tomography, energy-dispersive neutron tomography, and in vivo X-ray cine-radiography.
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11
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Arledge CA, Crowe WN, Wang L, Bourland JD, Topaloglu U, Habib AA, Zhao D. Transfer Learning Approach to Vascular Permeability Changes in Brain Metastasis Post-Whole-Brain Radiotherapy. Cancers (Basel) 2023; 15:2703. [PMID: 37345039 DOI: 10.3390/cancers15102703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 04/28/2023] [Accepted: 05/08/2023] [Indexed: 06/23/2023] Open
Abstract
The purpose of this study is to further validate the utility of our previously developed CNN in an alternative small animal model of BM through transfer learning. Unlike the glioma model, the BM mouse model develops multifocal intracranial metastases, including both contrast enhancing and non-enhancing lesions on DCE MRI, thus serving as an excellent brain tumor model to study tumor vascular permeability. Here, we conducted transfer learning by transferring the previously trained GBM CNN to DCE MRI datasets of BM mice. The CNN was re-trained to learn about the relationship between BM DCE images and target permeability maps extracted from the Extended Tofts Model (ETM). The transferred network was found to accurately predict BM permeability and presented with excellent spatial correlation with the target ETM PK maps. The CNN model was further tested in another cohort of BM mice treated with WBRT to assess vascular permeability changes induced via radiotherapy. The CNN detected significantly increased permeability parameter Ktrans in WBRT-treated tumors (p < 0.01), which was in good agreement with the target ETM PK maps. In conclusion, the proposed CNN can serve as an efficient and accurate tool for characterizing vascular permeability and treatment responses in small animal brain tumor models.
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Affiliation(s)
- Chad A Arledge
- Department of Biomedical Engineering, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
| | - William N Crowe
- Department of Engineering, Wake Forest University, Winston-Salem, NC 27101, USA
| | - Lulu Wang
- Department of Biomedical Engineering, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
| | - John Daniel Bourland
- Department of Radiation Oncology, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
| | - Umit Topaloglu
- Department of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
- Clinical and Translation Research Informatics Branch, National Cancer Institute, Rockville, MD 20850, USA
| | - Amyn A Habib
- Department of Neurology, University of Texas Southwestern Medical Center and VA North Texas Medical Center, Dallas, TX 75390, USA
| | - Dawen Zhao
- Department of Biomedical Engineering, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
- Department of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
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12
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Rajapakse D, Meckstroth J, Jantz DT, Camarda KV, Yao Z, Leonard KC. Deconvoluting Kinetic Rate Constants of Catalytic Substrates from Scanning Electrochemical Approach Curves with Artificial Neural Networks. ACS MEASUREMENT SCIENCE AU 2023; 3:103-112. [PMID: 37090257 PMCID: PMC10120032 DOI: 10.1021/acsmeasuresciau.2c00056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 10/28/2022] [Accepted: 10/31/2022] [Indexed: 05/03/2023]
Abstract
Extracting information from experimental measurements in the chemical sciences typically requires curve fitting, deconvolution, and/or solving the governing partial differential equations via numerical (e.g., finite element analysis) or analytical methods. However, using numerical or analytical methods for high-throughput data analysis typically requires significant postprocessing efforts. Here, we show that deep learning artificial neural networks can be a very effective tool for extracting information from experimental data. As an example, reactivity and topography information from scanning electrochemical microscopy (SECM) approach curves are highly convoluted. This study utilized multilayer perceptrons and convolutional neural networks trained on simulated SECM data to extract kinetic rate constants of catalytic substrates. Our key findings were that multilayer perceptron models performed very well when the experimental data were close to the ideal conditions with which the model was trained. However, convolutional neural networks, which analyze images as opposed to direct data, were able to accurately predict the kinetic rate constant of Fe-doped nickel (oxy)hydroxide catalyst at different applied potentials even though the experimental approach curves were not ideal. Due to the speed at which machine learning models can analyze data, we believe this study shows that artificial neural networks could become powerful tools in high-throughput data analysis.
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Affiliation(s)
- Dinuka Rajapakse
- Department
of Chemical & Petroleum Engineering, The University of Kansas, 4132 Learned Hall, 1530 West 15th Street, Lawrence, Kansas66045, United States
- Center
for Environmentally Beneficial Catalysis, The University of Kansas, LSRL Building A, Suite 110, 1501 Wakarusa Drive, Lawrence, Kansas66047, United States
| | - Josh Meckstroth
- Department
of Chemical & Petroleum Engineering, The University of Kansas, 4132 Learned Hall, 1530 West 15th Street, Lawrence, Kansas66045, United States
| | - Dylan T. Jantz
- Department
of Chemical & Petroleum Engineering, The University of Kansas, 4132 Learned Hall, 1530 West 15th Street, Lawrence, Kansas66045, United States
- Center
for Environmentally Beneficial Catalysis, The University of Kansas, LSRL Building A, Suite 110, 1501 Wakarusa Drive, Lawrence, Kansas66047, United States
| | - Kyle Vincent Camarda
- Department
of Chemical & Petroleum Engineering, The University of Kansas, 4132 Learned Hall, 1530 West 15th Street, Lawrence, Kansas66045, United States
| | - Zijun Yao
- Department
of Electrical Engineering & Computer Science, The University of Kansas, 2001 Eaton Hall, 1520 West 15th Street, Lawrence, Kansas66045, United States
| | - Kevin C. Leonard
- Department
of Chemical & Petroleum Engineering, The University of Kansas, 4132 Learned Hall, 1530 West 15th Street, Lawrence, Kansas66045, United States
- Center
for Environmentally Beneficial Catalysis, The University of Kansas, LSRL Building A, Suite 110, 1501 Wakarusa Drive, Lawrence, Kansas66047, United States
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13
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Emara HM, Shoaib MR, El-Shafai W, Elwekeil M, Hemdan EED, Fouda MM, Taha TE, El-Fishawy AS, El-Rabaie ESM, El-Samie FEA. Simultaneous Super-Resolution and Classification of Lung Disease Scans. Diagnostics (Basel) 2023; 13:diagnostics13071319. [PMID: 37046537 PMCID: PMC10093568 DOI: 10.3390/diagnostics13071319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 03/17/2023] [Accepted: 03/20/2023] [Indexed: 04/05/2023] Open
Abstract
Acute lower respiratory infection is a leading cause of death in developing countries. Hence, progress has been made for early detection and treatment. There is still a need for improved diagnostic and therapeutic strategies, particularly in resource-limited settings. Chest X-ray and computed tomography (CT) have the potential to serve as effective screening tools for lower respiratory infections, but the use of artificial intelligence (AI) in these areas is limited. To address this gap, we present a computer-aided diagnostic system for chest X-ray and CT images of several common pulmonary diseases, including COVID-19, viral pneumonia, bacterial pneumonia, tuberculosis, lung opacity, and various types of carcinoma. The proposed system depends on super-resolution (SR) techniques to enhance image details. Deep learning (DL) techniques are used for both SR reconstruction and classification, with the InceptionResNetv2 model used as a feature extractor in conjunction with a multi-class support vector machine (MCSVM) classifier. In this paper, we compare the proposed model performance to those of other classification models, such as Resnet101 and Inceptionv3, and evaluate the effectiveness of using both softmax and MCSVM classifiers. The proposed system was tested on three publicly available datasets of CT and X-ray images and it achieved a classification accuracy of 98.028% using a combination of SR and InceptionResNetv2. Overall, our system has the potential to serve as a valuable screening tool for lower respiratory disorders and assist clinicians in interpreting chest X-ray and CT images. In resource-limited settings, it can also provide a valuable diagnostic support.
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Affiliation(s)
- Heba M. Emara
- Department of Electronics and Communications Engineering, High Institute of Electronic Engineering, Ministry of Higher Education, Bilbis-Sharqiya 44621, Egypt
| | - Mohamed R. Shoaib
- School of Computer Science and Engineering (SCSE), Nanyang Technological University (NTU), Singapore 639798, Singapore
| | - Walid El-Shafai
- Security Engineering Lab, Computer Science Department, Prince Sultan University, Riyadh 11586, Saudi Arabia
- Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, Egypt
| | - Mohamed Elwekeil
- Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, Egypt
| | - Ezz El-Din Hemdan
- Department of Computer Science and Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, Egypt
| | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA
| | - Taha E. Taha
- Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, Egypt
| | - Adel S. El-Fishawy
- Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, Egypt
| | - El-Sayed M. El-Rabaie
- Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, Egypt
| | - Fathi E. Abd El-Samie
- Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, Egypt
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh 11564, Saudi Arabia
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14
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Wang Y, Zhang H, Wei Y, Wang H, Peng Y, Bin Z, Li W. An evolutionary computation-based machine learning for network attack detection in big data traffic. Appl Soft Comput 2023. [DOI: 10.1016/j.asoc.2023.110184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
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15
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Maiseli B. Nonlinear anisotropic diffusion methods for image denoising problems: Challenges and future research opportunities. ARRAY 2022. [DOI: 10.1016/j.array.2022.100265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
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16
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Li D, Deng H, Yao G, Jiang J, Zhang Y. Gamma Radiation Image Noise Prediction Method Based on Statistical Analysis and Random Walk. SENSORS (BASEL, SWITZERLAND) 2022; 22:7325. [PMID: 36236426 PMCID: PMC9573051 DOI: 10.3390/s22197325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 08/26/2022] [Accepted: 09/19/2022] [Indexed: 06/16/2023]
Abstract
The gamma radiation environment is one of the harshest operating environments for image acquisition systems, and the captured images are heavily noisy. In this paper, we improve the multi-frame difference method for the characteristics of noise and add an edge detection algorithm to segment the noise region and extract the noise quantization information. A Gaussian mixture model of the gamma radiation noise is then established by performing a specific statistical analysis of the amplitude and quantity information of the noise. The established model is combined with the random walk algorithm to generate noise and achieve the prediction of image noise under different accumulated doses. Evaluated by objective similarity matching, there is no significant difference between the predicted image noise and the actual noise in subjective perception. The ratio of similarity-matched images in the sample from the predicted noise to the actual noise reaches 0.908. To further illustrate the spillover effect of this research, in the discussion session, we used the predicted image noise as the training set input to a deep residual network for denoising. The network model was able to achieve a good denoising effect. The results show that the prediction method proposed in this paper can accomplish the prediction of gamma radiation image noise, which is beneficial to the elimination of image noise in this environment.
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Affiliation(s)
- Dongjie Li
- Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, Harbin University of Science and Technology, Harbin 150080, China
- Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, China
| | - Haipeng Deng
- Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, Harbin University of Science and Technology, Harbin 150080, China
- Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, China
| | - Gang Yao
- Heilongjiang Institute of Atomic Energy, Harbin 150086, China
| | - Jicheng Jiang
- Heilongjiang Institute of Atomic Energy, Harbin 150086, China
| | - Yubao Zhang
- Heilongjiang Institute of Atomic Energy, Harbin 150086, China
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17
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Object tracking in infrared images using a deep learning model and a target-attention mechanism. COMPLEX INTELL SYST 2022. [DOI: 10.1007/s40747-022-00872-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Abstract
AbstractSmall object tracking in infrared images is widely utilized in various fields, such as video surveillance, infrared guidance, and unmanned aerial vehicle monitoring. The existing small target detection strategies in infrared images suffer from submerging the target in heavy cluttered infrared (IR) maritime images. To overcome this issue, we use the original image and the corresponding encoded image to apply our model. We use the local directional number patterns algorithm to encode the original image to represent more unique details. Our model is able to learn more informative and unique features from the original and encoded image for visual tracking. In this study, we explore the best convolutional filters to obtain the best possible visual tracking results by finding those inactive to the backgrounds while active in the target region. To this end, the attention mechanism for the feature extracting framework is investigated comprising a scale-sensitive feature generation component and a discriminative feature generation module based on the gradients of regression and scoring losses. Comprehensive experiments have demonstrated that our pipeline obtains competitive results compared to recently published papers.
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18
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Dual Autoencoder Network with Separable Convolutional Layers for Denoising and Deblurring Images. J Imaging 2022; 8:jimaging8090250. [PMID: 36135415 PMCID: PMC9502178 DOI: 10.3390/jimaging8090250] [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: 07/11/2022] [Revised: 09/02/2022] [Accepted: 09/05/2022] [Indexed: 11/17/2022] Open
Abstract
A dual autoencoder employing separable convolutional layers for image denoising and deblurring is represented. Combining two autoencoders is presented to gain higher accuracy and simultaneously reduce the complexity of neural network parameters by using separable convolutional layers. In the proposed structure of the dual autoencoder, the first autoencoder aims to denoise the image, while the second one aims to enhance the quality of the denoised image. The research includes Gaussian noise (Gaussian blur), Poisson noise, speckle noise, and random impulse noise. The advantages of the proposed neural network are the number reduction in the trainable parameters and the increase in the similarity between the denoised or deblurred image and the original one. The similarity is increased by decreasing the main square error and increasing the structural similarity index. The advantages of a dual autoencoder network with separable convolutional layers are demonstrated by a comparison of the proposed network with a convolutional autoencoder and dual convolutional autoencoder.
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19
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Yamamoto T, Lacheret C, Fukutomi H, Kamraoui RA, Denat L, Zhang B, Prevost V, Zhang L, Ruet A, Triaire B, Dousset V, Coupé P, Tourdias T. Validation of a Denoising Method Using Deep Learning-Based Reconstruction to Quantify Multiple Sclerosis Lesion Load on Fast FLAIR Imaging. AJNR Am J Neuroradiol 2022; 43:1099-1106. [PMID: 35902124 PMCID: PMC9575422 DOI: 10.3174/ajnr.a7589] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 06/13/2022] [Indexed: 12/27/2022]
Abstract
BACKGROUND AND PURPOSE Accurate quantification of WM lesion load is essential for the care of patients with multiple sclerosis. We tested whether the combination of accelerated 3D-FLAIR and denoising using deep learning-based reconstruction could provide a relevant strategy while shortening the imaging examination. MATERIALS AND METHODS Twenty-eight patients with multiple sclerosis were prospectively examined using 4 implementations of 3D-FLAIR with decreasing scan times (4 minutes 54 seconds, 2 minutes 35 seconds, 1 minute 40 seconds, and 1 minute 15 seconds). Each FLAIR sequence was reconstructed without and with denoising using deep learning-based reconstruction, resulting in 8 FLAIR sequences per patient. Image quality was assessed with the Likert scale, apparent SNR, and contrast-to-noise ratio. Manual and automatic lesion segmentations, performed randomly and blindly, were quantitatively evaluated against ground truth using the absolute volume difference, true-positive rate, positive predictive value, Dice similarity coefficient, Hausdorff distance, and F1 score based on the lesion count. The Wilcoxon signed-rank test and 2-way ANOVA were performed. RESULTS Both image-quality evaluation and the various metrics showed deterioration when the FLAIR scan time was accelerated. However, denoising using deep learning-based reconstruction significantly improved subjective image quality and quantitative performance metrics, particularly for manual segmentation. Overall, denoising using deep learning-based reconstruction helped to recover contours closer to those from the criterion standard and to capture individual lesions otherwise overlooked. The Dice similarity coefficient was equivalent between the 2-minutes-35-seconds-long FLAIR with denoising using deep learning-based reconstruction and the 4-minutes-54-seconds-long reference FLAIR sequence. CONCLUSIONS Denoising using deep learning-based reconstruction helps to recognize multiple sclerosis lesions buried in the noise of accelerated FLAIR acquisitions, a possibly useful strategy to efficiently shorten the scan time in clinical practice.
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Affiliation(s)
- T Yamamoto
- From the Institut de Bio-imagerie (T.Y., H.F., L.D., V.D., T.T.), University Bordeaux, Bordeaux, France
| | - C Lacheret
- Neuroimagerie Diagnostique et Thérapeutique (C.L., V.D., T.T.)
| | - H Fukutomi
- From the Institut de Bio-imagerie (T.Y., H.F., L.D., V.D., T.T.), University Bordeaux, Bordeaux, France
| | - R A Kamraoui
- Laboratoire Bordelais de Recherche en Informatique (R.A.K., P.C.), University Bordeaux, Le Centre National de la Recherche Scientifique, Bordeaux Institut National Polytechnique, Talence, France
| | - L Denat
- From the Institut de Bio-imagerie (T.Y., H.F., L.D., V.D., T.T.), University Bordeaux, Bordeaux, France
| | - B Zhang
- Canon Medical Systems Europe (B.Z.), Zoetermeer, the Netherlands
| | - V Prevost
- Canon Medical Systems (V.P., B.T.), Tochigi, Japan
| | - L Zhang
- Canon Medical Systems China (L.Z.), Beijing, China
| | - A Ruet
- Service de Neurologie (A.R.), Centre Hospitalier Universitaire de Bordeaux, Bordeaux, France
| | - B Triaire
- Canon Medical Systems (V.P., B.T.), Tochigi, Japan
| | - V Dousset
- From the Institut de Bio-imagerie (T.Y., H.F., L.D., V.D., T.T.), University Bordeaux, Bordeaux, France.,Neuroimagerie Diagnostique et Thérapeutique (C.L., V.D., T.T.).,NeurocentreMagendie (V.D., T.T.), University of Bordeaux, L'Institut National de la Santé et de la Recherche Médicale, Bordeaux, France
| | - P Coupé
- Laboratoire Bordelais de Recherche en Informatique (R.A.K., P.C.), University Bordeaux, Le Centre National de la Recherche Scientifique, Bordeaux Institut National Polytechnique, Talence, France
| | - T Tourdias
- From the Institut de Bio-imagerie (T.Y., H.F., L.D., V.D., T.T.), University Bordeaux, Bordeaux, France .,Neuroimagerie Diagnostique et Thérapeutique (C.L., V.D., T.T.).,NeurocentreMagendie (V.D., T.T.), University of Bordeaux, L'Institut National de la Santé et de la Recherche Médicale, Bordeaux, France
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20
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Kalejahi BK, Meshgini S, Danishvar S, Khorram S. Diagnosis of liver disease by computer- assisted imaging techniques: A literature review. INTELL DATA ANAL 2022. [DOI: 10.3233/ida-216379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Diagnosis of liver disease using computer-aided detection (CAD) systems is one of the most efficient and cost-effective methods of medical image diagnosis. Accurate disease detection by using ultrasound images or other medical imaging modalities depends on the physician’s or doctor’s experience and skill. CAD systems have a critical role in helping experts make accurate and right-sized assessments. There are different types of CAD systems for diagnosing different diseases, and one of the applications is in liver disease diagnosis and detection by using intelligent algorithms to detect any abnormalities. Machine learning and deep learning algorithms and models play also a big role in this area. In this article, we tried to review the techniques which are utilized in different stages of CAD systems and pursue the methods used in preprocessing, extracting, and selecting features and classification. Also, different techniques are used to segment and analyze the liver ultrasound medical images, which is still a challenging approach to how to use these techniques and their technical and clinical effectiveness as a global approach.
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Affiliation(s)
- Behnam Kiani Kalejahi
- Department of Biomedical Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
| | - Saeed Meshgini
- Department of Biomedical Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
| | - Sebelan Danishvar
- Department of Electronics and Computer Engineering, College of Engineering, Design and Physical Sciences, Brunel University, UK
| | - Sara Khorram
- Department of Computer Engineering, Faculty of Engineering, Alzahra University, Tehran, Iran
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21
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Krishnan AM, Bouazizi M, Ohtsuki T. An Infrared Array Sensor-Based Approach for Activity Detection, Combining Low-Cost Technology with Advanced Deep Learning Techniques. SENSORS 2022; 22:s22103898. [PMID: 35632305 PMCID: PMC9145665 DOI: 10.3390/s22103898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 05/17/2022] [Accepted: 05/18/2022] [Indexed: 11/16/2022]
Abstract
In this paper, we propose an activity detection system using a 24 × 32 resolution infrared array sensor placed on the ceiling. We first collect the data at different resolutions (i.e., 24 × 32, 12 × 16, and 6 × 8) and apply the advanced deep learning (DL) techniques of Super-Resolution (SR) and denoising to enhance the quality of the images. We then classify the images/sequences of images depending on the activities the subject is performing using a hybrid deep learning model combining a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM). We use data augmentation to improve the training of the neural networks by incorporating a wider variety of samples. The process of data augmentation is performed by a Conditional Generative Adversarial Network (CGAN). By enhancing the images using SR, removing the noise, and adding more training samples via data augmentation, our target is to improve the classification accuracy of the neural network. Through experiments, we show that employing these deep learning techniques to low-resolution noisy infrared images leads to a noticeable improvement in performance. The classification accuracy improved from 78.32% to 84.43% (for images with 6 × 8 resolution), and from 90.11% to 94.54% (for images with 12 × 16 resolution) when we used the CNN and CNN + LSTM networks, respectively.
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Affiliation(s)
| | - Mondher Bouazizi
- Faculty of Science and Technology, Keio University, Yokohama 223-8522, Japan;
| | - Tomoaki Ohtsuki
- Faculty of Science and Technology, Keio University, Yokohama 223-8522, Japan;
- Correspondence:
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22
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Yu H, Adhikari RX. Nonlinear Noise Cleaning in Gravitational-Wave Detectors With Convolutional Neural Networks. Front Artif Intell 2022; 5:811563. [PMID: 35372828 PMCID: PMC8969740 DOI: 10.3389/frai.2022.811563] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 02/08/2022] [Indexed: 11/24/2022] Open
Abstract
Currently, the sub-60 Hz sensitivity of gravitational-wave (GW) detectors like Advanced LIGO (aLIGO) is limited by the control noises from auxiliary degrees of freedom which nonlinearly couple to the main GW readout. One promising way to tackle this challenge is to perform nonlinear noise mitigation using convolutional neural networks (CNNs), which we examine in detail in this study. In many cases, the noise coupling is bilinear and can be viewed as a few fast channels' outputs modulated by some slow channels. We show that we can utilize this knowledge of the physical system and adopt an explicit “slow×fast” structure in the design of the CNN to enhance its performance of noise subtraction. We then examine the requirements in the signal-to-noise ratio (SNR) in both the target channel (i.e., the main GW readout) and in the auxiliary sensors in order to reduce the noise by at least a factor of a few. In the case of limited SNR in the target channel, we further demonstrate that the CNN can still reach a good performance if we use curriculum learning techniques, which in reality can be achieved by combining data from quiet times and those from periods with active noise injections.
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Affiliation(s)
- Hang Yu
- TAPIR, Walter Burke Institute for Theoretical Physics, MC 350-17, California Institute of Technology, Pasadena, CA, United States
- *Correspondence: Hang Yu
| | - Rana X. Adhikari
- LIGO Laboratory, MC 100-36, California Institute of Technology, Pasadena, CA, United States
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23
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Bizhani M, Ardakani OH, Little E. Reconstructing high fidelity digital rock images using deep convolutional neural networks. Sci Rep 2022; 12:4264. [PMID: 35277546 PMCID: PMC8917167 DOI: 10.1038/s41598-022-08170-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 03/03/2022] [Indexed: 01/16/2023] Open
Abstract
Imaging methods have broad applications in geosciences. Scanning electron microscopy (SEM) and micro-CT scanning have been applied for studying various geological problems. Despite significant advances in imaging capabilities, and image processing algorithms, acquiring high-quality data from images is still challenging and time-consuming.
Obtaining a 3D representative volume for a tight rock sample takes days to weeks. Image artifacts such as noise further complicate the use of imaging methods for the determination of rock properties. In this study, we present applications of several convolutional neural networks (CNN) for rapid image denoising, deblurring and super-resolving digital rock images. Such an approach enables rapid imaging of larger samples, which in turn improves the statistical relevance of the subsequent analysis. We demonstrate the application of several CNNs for image restoration applicable to scientific imaging. The results show that images can be denoised without a priori knowledge of the noise with great confidence. Furthermore, we show how attaching several CNNs in an end-to-end fashion can improve the final quality of reconstruction. Our experiments with SEM and CT scan images of several rock types show image denoising, deblurring and super-resolution can be performed simultaneously.
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Affiliation(s)
- Majid Bizhani
- Natural Resources Canada, Geological Survey of Canada, 3303 33 Street NW, Calgary, AB, T2L 2A7, Canada.
| | - Omid Haeri Ardakani
- Natural Resources Canada, Geological Survey of Canada, 3303 33 Street NW, Calgary, AB, T2L 2A7, Canada.,Department of Geoscience, University of Calgary, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada
| | - Edward Little
- Natural Resources Canada, Geological Survey of Canada, 3303 33 Street NW, Calgary, AB, T2L 2A7, Canada
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24
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A Convolutional Autoencoder Topology for Classification in High-Dimensional Noisy Image Datasets. SENSORS 2021; 21:s21227731. [PMID: 34833805 PMCID: PMC8622369 DOI: 10.3390/s21227731] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 11/14/2021] [Accepted: 11/18/2021] [Indexed: 11/17/2022]
Abstract
Deep convolutional neural networks have shown remarkable performance in the image classification domain. However, Deep Learning models are vulnerable to noise and redundant information encapsulated into the high-dimensional raw input images, leading to unstable and unreliable predictions. Autoencoders constitute an unsupervised dimensionality reduction technique, proven to filter out noise and redundant information and create robust and stable feature representations. In this work, in order to resolve the problem of DL models' vulnerability, we propose a convolutional autoencoder topological model for compressing and filtering out noise and redundant information from initial high dimensionality input images and then feeding this compressed output into convolutional neural networks. Our results reveal the efficiency of the proposed approach, leading to a significant performance improvement compared to Deep Learning models trained with the initial raw images.
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25
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Cho J, Zhang J, Spincemaille P, Zhang H, Hubertus S, Wen Y, Jafari R, Zhang S, Nguyen TD, Dimov AV, Gupta A, Wang Y. QQ-NET - using deep learning to solve quantitative susceptibility mapping and quantitative blood oxygen level dependent magnitude (QSM+qBOLD or QQ) based oxygen extraction fraction (OEF) mapping. Magn Reson Med 2021; 87:1583-1594. [PMID: 34719059 DOI: 10.1002/mrm.29057] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Revised: 09/01/2021] [Accepted: 10/07/2021] [Indexed: 01/17/2023]
Abstract
PURPOSE To improve accuracy and speed of quantitative susceptibility mapping plus quantitative blood oxygen level-dependent magnitude (QSM+qBOLD or QQ) -based oxygen extraction fraction (OEF) mapping using a deep neural network (QQ-NET). METHODS The 3D multi-echo gradient echo images were acquired in 34 ischemic stroke patients and 4 healthy subjects. Arterial spin labeling and diffusion weighted imaging (DWI) were also performed in the patients. NET was developed to solve the QQ model inversion problem based on Unet. QQ-based OEF maps were reconstructed with previously introduced temporal clustering, tissue composition, and total variation (CCTV) and NET. The results were compared in simulation, ischemic stroke patients, and healthy subjects using a two-sample Kolmogorov-Smirnov test. RESULTS In the simulation, QQ-NET provided more accurate and precise OEF maps than QQ-CCTV with 150 times faster reconstruction speed. In the subacute stroke patients, OEF from QQ-NET had greater contrast-to-noise ratio (CNR) between DWI-defined lesions and their unaffected contralateral normal tissue than with QQ-CCTV: 1.9 ± 1.3 vs 6.6 ± 10.7 (p = 0.03). In healthy subjects, both QQ-CCTV and QQ-NET provided uniform OEF maps. CONCLUSION QQ-NET improves the accuracy of QQ-based OEF with faster reconstruction.
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Affiliation(s)
- Junghun Cho
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Jinwei Zhang
- Department of Biomedical Engineering, Cornell University, Ithaca, New York, USA
| | - Pascal Spincemaille
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Hang Zhang
- Department of Biomedical Engineering, Cornell University, Ithaca, New York, USA
| | - Simon Hubertus
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Yan Wen
- Department of Biomedical Engineering, Cornell University, Ithaca, New York, USA
| | - Ramin Jafari
- Department of Biomedical Engineering, Cornell University, Ithaca, New York, USA
| | - Shun Zhang
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Thanh D Nguyen
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Alexey V Dimov
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Ajay Gupta
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Yi Wang
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA.,Department of Biomedical Engineering, Cornell University, Ithaca, New York, USA
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