Blood Vessel Segmentation from Color Retinal Images Using Unsupervised Texture Classification

Automated blood vessel segmentation is an important issue for assessing retinal abnormalities and diagnoses of many diseases. The segmentation of vessels is complicated by huge variations in local contrast, particularly in case of the minor vessels. The method of texture based vessel segmentation to overcome this problem. A bank of Gabor energy filters are used to analyze the texture features from which a feature vector is constructed for each pixel. The Fuzzy C-Means (FCM) clustering algorithm is used to classify the feature vectors into vessels or non-vessel based on the texture properties. From the FCM clustering output we attain the final output segmented image after a post processing step.