| Paper: | WA-P8.7 |
| Session: | Biomedical Imaging IV: Segmentation and Quantitative Analysis |
| Time: | Wednesday, September 19, 09:50 - 12:30 |
| Presentation: |
Poster
|
| Title: |
BLOOD VESSEL SEGMENTATION FROM COLOR RETINAL IMAGES USING UNSUPERVISED TEXTURE CLASSIFICATION |
| Authors: |
Alauddin Bhuiyan; University of Melbourne | | |
| | Baikunth Nath; University of Melbourne | | |
| | Joselito Chua; University of Melbourne | | |
| | Ramamohanarao Kotagiri; University of Melbourne | | |
| 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. In this paper, we propose a new method of texture based vessel segmentation to overcome this problem. We use Gaussian and L*a*b* perceptually uniform color spaces with original RGB for texture feature extraction on retinal images. 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 vessel 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. We compare our method with hand-labeled ground truth segmentation of five images and achieve 84.37% sensitivity and 99.61% specificity. |
