Abstract—In this paper, we propose a method for segmenting
blood vessels from retinal images. We extract two sets of
features for image classification: features based on Gabor
wavelet and line operator. At each pixel of retinal image we
construct a feature vector consisting of the pixel intensity, four
features from Gabor wavelet transform in different scales and
two features from orthogonal line operators. We compare the
result of classification using two classifiers: Bayesian and SVM.
First we estimate class-conditional probability density functions
for vessel and non-vessel using Gaussian mixture model. Then
using a Bayesian classifier we implement a fast classification.
The result of experiments show the combination of Gabor
features and line features provides a good performance for
vessel segmentation. We tested the proposed algorithm on
DRIVE database which is publicly available. As the second
classifier we employ Support Vector Machine. The results
shows SVM classifier in some cases performs better than
Index Terms—Retinal image, vessel segmentation, Gabor wavelet, line detector, supervised classification.
The authors are with the Electrical and robotic engineering, Shahrood university of technology Shahrood, Iran (e-mail: firstname.lastname@example.org; email@example.com).
Cite:Reza Kharghanian and Alireza Ahmadyfard, "Retinal Blood Vessel Segmentation Using Gabor Wavelet and Line Operator," International Journal of Machine Learning and Computing vol.2, no. 5, pp. 593-597, 2012.