Segmentation of retinal vessels with a hysteresis binary-classification paradigm

• Published in: Computerized medical imaging and graphics Vol. 36; no. 4; pp. 325 - 335
• Main Authors: Condurache, Alexandru Paul, Mertins, Alfred
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.06.2012
• Subjects: Algorithms; Binary classification; Feature selection; Humans; Hysteresis methods; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image segmentation; Internal Medicine; Other; Pattern Recognition, Automated - methods; Photography; Retina vessels; Retinal Vessels - anatomy & histology; Vessel enhancement; Image segmentation; Feature selection; Vessel enhancement; Retina vessels; Binary classification; Hysteresis methods
• ISSN: 0895-6111; 1879-0771
• DOI: 10.1016/j.compmedimag.2012.02.002

Abstract Vessel segmentation in photographies of the retina is needed in a set of computer-supported medical applications related to diagnosis and surgery planning. Considering each pixel in an image as a point in a feature space, segmentation is a binary classification problem where pixels need to be assigned to one of two classes: object and background. We describe a paradigm of hysteresis-classifier design that we apply to the problem of vessel segmentation. Before classification, a multidimensional feature vector is computed for each pixel, such that in the corresponding feature space, vessels and background are more separable than in the original image space. Several classifiers that stem from the hysteresis-classifier design paradigm are tested with this feature space on publicly available databases. These classifiers are very fast and achieve results that are comparable or even superior to known dedicated methods. Hysteresis-based classifiers represent a fast and accurate solution for the retinal-vessel segmentation problem.