Blood Vessel Segmentation from Color Retinal Images Using Unsupervised Texture Classification
Abstract: 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.
Keywords: Medical image, texture classification, Gabor energy filter bank, FCM clustering, image segmentation
How to Cite?: Vaibhavi B. Patil, "Blood Vessel Segmentation from Color Retinal Images Using Unsupervised Texture Classification", Volume 4 Issue 3, March 2015, International Journal of Science and Research (IJSR), Pages: 336-338, https://www.ijsr.net/getabstract.php?paperid=SUB151981, DOI: https://dx.doi.org/10.21275/SUB151981