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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
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[ACM Press the 2nd International Conference - Sydney, NSW, Australia (2018.10.06-2018.10.08)] Proceedings of the 2nd International Conference on Graphics and Signal Processing - ICGSP'18 - A Comparative Analysis of Clustering Algorithms for Ultrasound Image Despeckling Applications
摘要： This paper proposes a novel framework for speckle noise suppression and edge preservation using clustering algorithms in ultrasound images. The algorithms considered are K-means clustering, fuzzy C-means clustering, possibilistic C-means, fuzzy possibilistic C-means, and possibilistic fuzzy C-means clustering. This work presents an exhaustive comparative analysis of the above clustering algorithms to consider their suitability for despeckling and identifies the best clustering algorithm. Two types of dataset are considered: medical ultrasound images of the thyroid, and synthetically modelled ultrasound images. The framework consists of several distinct phases - first the edges of the image are identified using the Canny edge operator, and then a clustering algorithm applied on high frequency coefficients extracted using wavelet transform. Finally, the preserved edges are added back to speckle suppressed image. Thus, the proposed clustering method effectively accomplishes both speckle suppression and edge preservation. This paper also presents a quantitative evaluation of results to demonstrate the effectiveness of the clustering approach.
关键词： speckle noise,Image quality metrics,Wavelet transform,Ultrasound image analysis,Canny edge detector,Clustering algorithms
更新于2025-09-04 15:30:14