Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
An improved matched filter for blood vessel detection of digital retinal images
Computers in Biology and Medicine
Extraction of blood vessels in ophthalmic color images of human retinas
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
IEEE Transactions on Information Technology in Biomedicine
Different averages of a fuzzy set with an application to vessel segmentation
IEEE Transactions on Fuzzy Systems
Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation
IEEE Transactions on Image Processing
An automated hybrid technique for detecting the stage of non-proliferative diabetic retinopathy
Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia
Content based human retinal image retrieval using vascular feature extraction
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part II
Blood vessel segmentation methodologies in retinal images - A survey
Computer Methods and Programs in Biomedicine
Non-Compactness Attribute Filtering to Extract Retinal Blood Vessels in Fundus Images
International Journal of E-Health and Medical Communications
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The extraction of blood vessels from retinal images is an important and challenging task in medical analysis and diagnosis. This paper presents a novel hybrid automatic approach for the extraction of retinal image vessels. The method consists in the application of mathematical morphology and a fuzzy clustering algorithm followed by a purification procedure. In mathematical morphology, the retinal image is smoothed and strengthened so that the blood vessels are enhanced and the background information is suppressed. The fuzzy clustering algorithm is then employed to the previous enhanced image for segmentation. After the fuzzy segmentation, a purification procedure is used to reduce the weak edges and noise, and the final results of the blood vessels are consequently achieved. The performance of the proposed method is compared with some existing segmentation methods and hand-labeled segmentations. The approach has been tested on a series of retinal images, and experimental results show that our technique is promising and effective.