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Nath MK, Dandapat S, Barna C. Automatic detection of blood vessels and evaluation of retinal disorder from color fundus images. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-179687] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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A novel diagnostic information based framework for super-resolution of retinal fundus images. Comput Med Imaging Graph 2019; 72:22-33. [PMID: 30772075 DOI: 10.1016/j.compmedimag.2019.01.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Revised: 12/04/2018] [Accepted: 01/15/2019] [Indexed: 11/23/2022]
Abstract
Advancements in tele-medicine have led to the development of portable and cheap hand-held retinal imaging devices. However, the images obtained from these devices have low resolution (LR) and poor quality that may not be suitable for retinal disease diagnosis. Therefore, this paper proposes a novel framework for the super-resolution (SR) of the LR fundus images. The method takes into consideration the diagnostic information in the fundus images during the SR process. In this work, SR is performed on the zone of interest of the fundus images. Clinical information of the selected zone is captured using the Shannon entropy, the contrast sensitivity index (CSI), the multi-resolution (MR) intra-band energy and the MR inter-band eigen features. The support vector machine (SVM) classifier is used to decide the clinical significance of the zone. Highly accurate learning based SR method or the bicubic interpolation is applied to the selected zone based on the classification output. The method is tested on the Standard Diabetic Retinopathy Database Calibration level 1 (DIARETDB1) and the Digital Retinal Images for Vessel Extraction (DRIVE) databases. Classification accuracy of 85.22% and 85.77% is achieved for the DIARETDB1 and DRIVE databases respectively. The SR performance of the algorithm is quantified in terms of peak signal to noise ratio (PSNR), structural similarity index measure (SSIM) and computational time. The proposed diagnostic information based SR achieves computational time efficiency without compromising with the high resolution (HR) reconstruction accuracy of the fundus image zones.
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Abdel-Hamid L, El-Rafei A, El-Ramly S, Michelson G, Hornegger J. Retinal image quality assessment based on image clarity and content. JOURNAL OF BIOMEDICAL OPTICS 2016; 21:96007. [PMID: 27637005 DOI: 10.1117/1.jbo.21.9.096007] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2016] [Accepted: 08/29/2016] [Indexed: 06/06/2023]
Abstract
Retinal image quality assessment (RIQA) is an essential step in automated screening systems to avoid misdiagnosis caused by processing poor quality retinal images. A no-reference transform-based RIQA algorithm is introduced that assesses images based on five clarity and content quality issues: sharpness, illumination, homogeneity, field definition, and content. Transform-based RIQA algorithms have the advantage of considering retinal structures while being computationally inexpensive. Wavelet-based features are proposed to evaluate the sharpness and overall illumination of the images. A retinal saturation channel is designed and used along with wavelet-based features for homogeneity assessment. The presented sharpness and illumination features are utilized to assure adequate field definition, whereas color information is used to exclude nonretinal images. Several publicly available datasets of varying quality grades are utilized to evaluate the feature sets resulting in area under the receiver operating characteristic curve above 0.99 for each of the individual feature sets. The overall quality is assessed by a classifier that uses the collective features as an input vector. The classification results show superior performance of the algorithm in comparison to other methods from literature. Moreover, the algorithm addresses efficiently and comprehensively various quality issues and is suitable for automatic screening systems.
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Affiliation(s)
- Lamiaa Abdel-Hamid
- Misr International University, Department of Electronics and Communication, Faculty of Engineering, Ismalia Road km28, Cairo, Egypt
| | - Ahmed El-Rafei
- Ain Shams University, Department of Engineering Physics and Mathematics, Faculty of Engineering, 1 El-Sarayat Street, Abbasia, Cairo 11517, Egypt
| | - Salwa El-Ramly
- Ain Shams University, Department of Electronics and Communication, Faculty of Engineering, 1 El-Sarayat Street, Abbasia, Cairo 11517, Egypt
| | - Georg Michelson
- Friedrich-Alexander University of Erlangen-Nuremberg, Department of Ophthalmology, Schwabachanlage 6, Erlangen 91054, GermanyeTalkingeyes & More GmbH, Medical Valley Center, Erlangen 91052, Germany
| | - Joachim Hornegger
- Friedrich-Alexander University of Erlangen-Nuremberg, Pattern Recognition Lab, Department of Computer Science, Martensstr. 3, Erlangen 91058, Germany
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Automated diagnosis of Age-related Macular Degeneration using greyscale features from digital fundus images. Comput Biol Med 2014; 53:55-64. [DOI: 10.1016/j.compbiomed.2014.07.015] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2014] [Revised: 07/04/2014] [Accepted: 07/20/2014] [Indexed: 01/19/2023]
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Veiga D, Pereira C, Ferreira M, Gonçalves L, Monteiro J. Quality evaluation of digital fundus images through combined measures. J Med Imaging (Bellingham) 2014; 1:014001. [PMID: 26158021 DOI: 10.1117/1.jmi.1.1.014001] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2013] [Revised: 02/14/2014] [Accepted: 03/10/2014] [Indexed: 11/14/2022] Open
Abstract
The evaluation of image quality is an important step before an automatic analysis of retinal images. Several conditions can impair the acquisition of a good image, and minimum image quality requirements should be present to ensure that an automatic or semiautomatic system provides an accurate diagnosis. A method to classify fundus images as low or good quality is presented. The method starts with the detection of regions of uneven illumination and evaluates if the segmented noise masks affect a clinically relevant area (around the macula). Afterwards, focus is evaluated through a fuzzy classifier. An input vector is created extracting three focus features. The system was validated in a large dataset (1454 fundus images), obtained from an online database and an eye clinic and compared with the ratings of three observers. The system performance was close to optimal with an area under the receiver operating characteristic curve of 0.9943.
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Affiliation(s)
- Diana Veiga
- University of Minho , Centro Algoritmi, Azurém, Guimarães 4800-025, Portugal ; ENERMETER , Parque Industrial de Celeirós 2ª Fase, Lugar de Gaião, Lotes 5/6, Braga 4705-025, Portugal
| | - Carla Pereira
- ENERMETER , Parque Industrial de Celeirós 2ª Fase, Lugar de Gaião, Lotes 5/6, Braga 4705-025, Portugal
| | - Manuel Ferreira
- University of Minho , Centro Algoritmi, Azurém, Guimarães 4800-025, Portugal ; ENERMETER , Parque Industrial de Celeirós 2ª Fase, Lugar de Gaião, Lotes 5/6, Braga 4705-025, Portugal
| | - Luís Gonçalves
- Oftalmocenter , Rua Francisco Ribeiro de Castro, n° 205, Azurém, Guimarães 4800-045, Portugal
| | - João Monteiro
- University of Minho , Centro Algoritmi, Azurém, Guimarães 4800-025, Portugal
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Fathi A, Naghsh-Nilchi AR. Automatic wavelet-based retinal blood vessels segmentation and vessel diameter estimation. Biomed Signal Process Control 2013. [DOI: 10.1016/j.bspc.2012.05.005] [Citation(s) in RCA: 68] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Qin J, Reif R, Zhi Z, Dziennis S, Wang R. Hemodynamic and morphological vasculature response to a burn monitored using a combined dual-wavelength laser speckle and optical microangiography imaging system. BIOMEDICAL OPTICS EXPRESS 2012; 3:455-66. [PMID: 22435094 PMCID: PMC3296534 DOI: 10.1364/boe.3.000455] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2011] [Revised: 01/14/2012] [Accepted: 01/16/2012] [Indexed: 05/03/2023]
Abstract
A multi-functional imaging system capable of determining relative changes in blood flow, hemoglobin concentration, and morphological features of the blood vasculature is demonstrated. The system combines two non-invasive imaging techniques, a dual-wavelength laser speckle contrast imaging (2-LSI) and an optical microangiography (OMAG) system. 2-LSI is used to monitor the changes in the dynamic blood flow and the changes in the concentration of oxygenated (HbO), deoxygenated (Hb) and total hemoglobin (HbT). The OMAG system is used to acquire high resolution images of the functional blood vessel network. The vessel area density (VAD) is used to quantify the blood vessel network morphology, specifically the capillary recruitment. The proposed multi-functional system is employed to assess the blood perfusion status from a mouse pinna before and immediately after a burn injury. To our knowledge, this is the first non-invasive, non-contact and multifunctional imaging modality that can simultaneously measure variations of several blood perfusion parameters.
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Affiliation(s)
- Jia Qin
- Department of Bioengineering, University of Washington, 3720 15th Avenue NE, Seattle, Washington 98195, USA
- These authors contributed equally to this work
| | - Roberto Reif
- Department of Bioengineering, University of Washington, 3720 15th Avenue NE, Seattle, Washington 98195, USA
- These authors contributed equally to this work
| | - Zhongwei Zhi
- Department of Bioengineering, University of Washington, 3720 15th Avenue NE, Seattle, Washington 98195, USA
| | - Suzan Dziennis
- Department of Bioengineering, University of Washington, 3720 15th Avenue NE, Seattle, Washington 98195, USA
| | - Ruikang Wang
- Department of Bioengineering, University of Washington, 3720 15th Avenue NE, Seattle, Washington 98195, USA
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