Title :
Retinal vessel segmentation using system fuzzy and DBSCAN algorithm
Author :
Riazifar, Negar ; Saghapour, Ehsan
Author_Institution :
Dept. of Electr. Eng., Shiraz Univ., Shiraz, Iran
Abstract :
Retinal vessel segmentation used for the early diagnosis of retinal diseases such as hypertension, diabetes and glaucoma. There exist several methods for segmenting blood vessels from retinal images. The aim of this paper is to analyze the retinal vessel segmentation based on the clustering algorithm DBSCAN relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape. DBSCAN requires only one input parameter and a value for this parameter is suggested to the user. The performance of algorithm is compared and analyzed using a number of measures which include sensitivity and specificity. The specificity and sensitivity of this method is 5.36 and 3.82 respectively.
Keywords :
blood vessels; diseases; eye; fuzzy set theory; image segmentation; medical image processing; pattern clustering; DBSCAN algorithm; clustering algorithm; density-based notion; diabetes; glaucoma; hypertension; retinal disease diagnosis; retinal image; retinal vessel segmentation; system fuzzy; Algorithm design and analysis; Biomedical imaging; Blood vessels; Clustering algorithms; Image segmentation; Retinal vessels; Blood Vessel Segmentation; Clustering Algorithms; Medical Imaging; Retinal Images; System Fuzzy;
Conference_Titel :
Pattern Recognition and Image Analysis (IPRIA), 2015 2nd International Conference on
Conference_Location :
Rasht
Print_ISBN :
978-1-4799-8444-2
DOI :
10.1109/PRIA.2015.7161643
