Retinal vessel segmentation using system fuzzy and DBSCAN algorithm

Author

Riazifar, Negar ; Saghapour, Ehsan

Author_Institution

Dept. of Electr. Eng., Shiraz Univ., Shiraz, Iran

fYear

2015

fDate

11-12 March 2015

Firstpage

1

Lastpage

4

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition and Image Analysis (IPRIA), 2015 2nd International Conference on

Conference_Location

Rasht

Print_ISBN

978-1-4799-8444-2

Type

conf

DOI

10.1109/PRIA.2015.7161643

Filename

7161643