Paper Info
Title
Polar Run-Length Features in Segmentation of Retinal Blood Vessels
Abstract
Manual segmentation of retinal blood vessels in optic fundus images is a tiresome task. Several methods have previously been proposed for the automatic segmentation of retinal blood vessels. In this paper we propose a classifier-based method. First the images are preprocessed so that the within class variability of the vessel and background classes are minimized. Next, the image is scanned with a window of a certain size. Polar run-length matrices are simply created by transforming the windows into polar coordinates and then constructing conventional run length matrices. Two features are then extracted for each gray level value in the polar run length matrix. The feature vectors are then classified using a multilayer perceptron artificial neural network. The performance of the proposed method is compared with that of the human observers and with those methods previously reported in literature.
Year
DOI
Venue
2009
10.1109/ICDIP.2009.18
Bangkok
Keywords
Field
DocType
polar run-length features,certain size,conventional run length matrix,background class,retinal blood vessel,retinal blood vessels,automatic segmentation,classifier-based method,polar run-length matrix,manual segmentation,polar run length matrix,pixel,biomedical imaging,feature vector,image segmentation,polar coordinate,feature extraction,intelligent control,learning artificial intelligence,classification algorithms,artificial neural network,multilayer perceptron,gaussian processes,image classification,process control
Computer vision,Feature vector,Pattern recognition,Computer science,Segmentation,Feature extraction,Image segmentation,Polar coordinate system,Multilayer perceptron,Pixel,Artificial intelligence,Contextual image classification
Conference
ISBN
Citations 
PageRank 
978-0-7695-3565-4
2
0.39
References 
Authors
7
4
Name
Order
Citations
PageRank
S. H. Rezatofighi1101.12
Roodaki, A.220.39
Amir Pourmorteza3131.67
Hamid Soltanian-Zadeh424422.92