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[Lecture Notes in Computational Vision and Biomechanics] Computer Aided Intervention and Diagnostics in Clinical and Medical Images Volume 31 || Segmentation of Type II Diabetic Patient’s Retinal Blood Vessel to Diagnose Diabetic Retinopathy
摘要： Diabetic Retinopathy is one of the ophthalmic reasons for visual deficiency. The favored fixate of consideration is on the estimation of deviation in the breadth of the retinal veins and the new vessel development. To witness the progressions, segmentation has to be made primarily. A framework to improve the quality of the segmentation result over pathological retinal images is proposed. The proposed method uses adaptive histogram equalizer for preprocessing, pulse coupled neural Network model for automatic feature vector generation and extraction of the retinal blood vessels. The test result represents that the proposed method is enhanced than other retinal competitive methods. The evaluation of the proposed approach is executed over standard public DRIVE, STARE, REVIEW, HRF, and DRIONS fundus image datasets. The proposed technique improves the segmentation results in terms of sensitivity, specificity, and accuracy.
关键词： Fundus image,Feature extraction,Diabetic Retinopathy,Retinal blood vessel,Medical imaging
更新于2025-09-23 15:22:29
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BAT algorithm inspired retinal blood vessel segmentation
摘要： The automated extraction of retinal blood vessels is the course of action in the medical analysis of retinal diseases. The proposed methodology for the retinal vessel segmentation is based on BAT algorithm and random forest classifier. A feature vector of 40-dimensional including local, phase and morphological features is extracted and the feature set which minimises the classifier error is identified by BAT algorithm. The selected features are also identified as the dominant features in the classification. Performance of the proposed method is analysed by the publicly available databases such as digital retinal images for vessel extraction and structured analysis of the retina. The authors’ proposed method is highly sensitive to identify the blood vessels, in view of the fact that it corresponds to the ability of the method to identify the blood vessels correctly. BAT algorithm-based proposed method achieves very high sensitivity and accuracy of about 82.85 and 95.34%, respectively.
关键词： digital retinal images,retinal blood vessel segmentation,structured analysis of the retina,feature extraction,BAT algorithm,random forest classifier
更新于2025-09-23 15:21:21
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Modified Curvature-based Trigonometric Identities for Retinal Blood Vessel Tortuosity Measurement in Diabetic Retinopathy Fundus Images
摘要： In current clinical practice, there is no specific standard and grading system that can be used to measure the behaviour of the retinal blood vessel curvature. The retinal blood vessel curvature is measured based on clinical experiences. It is very subjective and inconsistent to describe the presence of tortuosity in fundus images. Thus, this paper aims to measure the tortuosity of retinal blood vessel using curvature-based method and investigate its relationship with diabetic retinopathy (DR) disease. The proposed tortuosity measures have been tested on 43 fundus images belonging to patients who have been diagnosed with DR disease and validated by two clinical experts from our collaborative hospital. On average, the proposed algorithm achieved 90.7% (accuracy), 98.72% (sensitivity) and 9.3% (false negative rate), that shows significant tortuosity presence in diabetic retinopathy fundus images.
关键词： Tortuosity,Retinal Blood Vessel,Digital Fundus Images,Diabetic Retinopathy,Curvature-based Method
更新于2025-09-10 09:29:36
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[IEEE 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) - Atlanta, GA (2017.10.21-2017.10.28)] 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) - Comparing Different Preprocessing Methods in Automated Segmentation of Retinal Vasculature
摘要： Computer methods and image processing provide medical doctors assistance at any time and relieve their work load, especially for iterative processes like identifying objects of interest such as lesions and anatomical structures from the image. Vescular detection is considered to be a crucial step in some retinal image analysis algorithms to find other retinal landmarks and lesions, and their corresponding diameters, to use as a length reference to measure objects in the retina. The objective of this study is to compare effect of two preprocessing methods on retinal vessel segmentation methods, Laplacian-of-Gaussian edge detector (using second-order spatial differentiation), Canny edge detector (estimating the gradient intensity), and Matched filter edge detector either in the normal fundus images or in the presence of retinal lesions like diabetic retinopathy. The steps for the segmentation are as following: 1) Smoothing: suppress as much noise as possible, without destroying the true edges, 2) Enhancement: apply a filter to enhance the quality of the edges in the image (sharpening), 3) Detection: determine which edge pixels should be discarded as noise and which should be retained by thresholding the edge strength and edge size, 4) Localization: determine the exact location of an edge by edge thinning or linking. From the accuracy view point, comparing to manual segmentation performed by ophthalmologists for retinal images belonging to a test set of 120 images, by using first preprocessing method, Illumination equalization, and contrast enhancement , the accuracy of Canny, Laplacian-of-Gaussian, and Match filter vessel segmentation was more than 85% for all databases (MUMS-DB, DRIVE, MESSIDOR). The performance of the segmentation methods using top-hat preprocessing (the second method) was more than 80%. And lastly, using matched filter had maximum accuracy for the vessel segmentation for all preprocessing steps for all databases.
关键词： contrast Enhancement,image processing,Diabetic retinopathy,top hat transformation,Laplacian-of-Gaussian edge detector,Illumination Equalization,retinal blood vessel,Match filter,Canny edge detector
更新于2025-09-09 09:28:46
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[IEEE 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) - Atlanta, GA (2017.10.21-2017.10.28)] 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) - Automated Optic Nerve Head Detection Based on Different Retinal Vasculature Segmentation Methods and Mathematical Morphology
摘要： Computer vision and image processing techniques provide important assistance to physicians and relieve their work load in different tasks. In particular, identifying objects of interest such as lesions and anatomical structures from the image is a challenging and iterative process that can be done by using computer vision and image processing approaches in a successful manner. Optic Nerve Head (ONH) detection is a crucial step in retinal image analysis algorithms. The goal of ONH detection is to ?nd and detect other retinal landmarks and lesions and their corresponding diameters, to use as a length reference to measure objects in the retina. The objective of this study is to apply three retinal vessel segmentation methods, Laplacian-of-Gaussian edge detector, Canny edge detector, and Matched ?lter edge detector for detection of the ONH either in the normal fundus images or in the presence of retinal lesions (e.g. diabetic retinopathy). The steps for the segmentation are as following: 1) Smoothing: suppress as much noise as possible, without destroying the true edges, 2) Enhancement: apply a ?lter to enhance the quality of the edges in the image (sharpening), 3) Detection: determine which edge pixels should be discarded as noise and which should be retained by thresholding the edge strength and edge size, 4) Localization: determine the exact location of an edge by edge thinning or linking. To evaluate the accuracy of our proposed method, we compare the output of our proposed method with the ground truth data that collected by ophthalmologists on retinal images belonging to a test set of 120 images. As shown in the results section, by using the Laplacian-of-Gaussian vessel segmentation, our automated algorithm ?nds 18 ONHs in true location for 20 color images in the CHASE-DB database and all images in the DRIVE database. For the Canny vessel segmentation, our automated algorithm ?nds 16 ONHs in true location for 20 images in the CHASE-DB database and 32 out of 40 images in the DRIVE database. And lastly, using matched ?lter in the vessel segmentation, our algorithm ?nds 19 ONHs in true location for 20 images in CHASE-DB database and all images in the DRIVE.
关键词： Laplacian-of-Gaussian edge detector,Diabetic retinopathy,Match ?lter,image processing,Optic Nerve Head,Canny edge detector,retinal blood vessel
更新于2025-09-09 09:28:46