Extraction of Blood Vessels in Retina

Extraction of Blood Vessels in Retina

Thamer Mitib Al Sariera (University of Mysore, Mysore, India) and Lalitha Rangarajan (University of Mysore, Mysore, India)
Copyright: © 2018 |Pages: 15
DOI: 10.4018/JITR.2018100108

Abstract

This article presents a novel method to extract retinal vascular tree automatically. The proposed method consists of four steps; smoothing image using low pass spatial filter to reduce spurious noise in the image; extracting candidate borders of the vessels based on a local window property; tracking process, starting with a candidate pixel and following in the optimum direction with monitoring the connectivity of the vessel twin border; constructing the whole tree of retinal blood vessels by connecting the vessel segments based on their spatial locations, widths and directions. The algorithm was trained with 20 images from the DRIVE dataset, and tested using the remaining 20 images.
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Numerous methods have been devised to address the segmentation of blood vessels in retinal images. (Hoover, Kouznetsova, & Goldbaum, 2000) proposed a method to segment the blood vessel by using local and region based properties at each pixel to detect the vascular tree. Pixels are classified as vessel or non-vessel by thresholding the image generated by a matched filter using a probing technique. Probing allows a pixel to be tested in multiple region configurations, before the final classification.

(Staal, Abràmoff, Niemeijer, Viergever, & Van Ginneken, 2004) used the notion of ridges for extracting the blood vessel. The properties of the ridges and the pixel considered forms the feature vector. Then K-nearest neighbor (KNN) classification was adopted for classifying the image ridges.

The method (Soares, Leandro, Cesar, Jelinek, & Cree, 2006) presented uses supervised classification. Each image pixel is classified as vessel or non-vessel using the pixel feature vector, which is composed of the pixel intensity and 2-D Gabor wavelet transform responses taken at multiple scales. Then a Bayesian classifier is applied to obtain the final segmentation.

(Singh, Kumar, & Srivastava, 2015) proposed an automatic local entropy thresholding retinal blood vessels segmentation method by modifying the standard Gaussian shaped matched filter. This method uses adaptive local thresholding to produce a binary image, then extract large connected components as large vessels (L. Xu & Luo, 2010).

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