Extraction of retinal blood vessels on fundus images by kirsch's template and Fuzzy C-Means

• Published in: Journal of medical physics Vol. 44; no. 1; pp. 21 - 26
• Main Authors: Jebaseeli, T, Durai, C, Peter, J
• Format: Journal Article
• Language: English
• Published: India Wolters Kluwer India Pvt. Ltd 01.01.2019; Medknow Publications and Media Pvt. Ltd; Medknow Publications & Media Pvt. Ltd; Medknow Publications & Media Pvt Ltd; Wolters Kluwer Medknow Publications
• Subjects: Accuracy; Algorithms; Blood vessels; Clustering; Diabetes; Diabetic retinopathy; Equalization; Feature extraction; fundus image; Fuzzy C-Means; Histograms; Image contrast; Image segmentation; Kirsch template; medical imaging; Medical personnel; Optimization; Original; Photography; Principal components analysis; Retina; retinal blood vessel segmentation; Surgery; Visual impairment; Diabetic retinopathy; Fuzzy C-Means; retinal blood vessel segmentation; Kirsch template; fundus image; medical imaging
• ISSN: 0971-6203; 1998-3913
• DOI: 10.4103/jmp.JMP_51_18

Purpose: Accurate segmentation of retinal blood vessel is an important task in computer-aided diagnosis and surgery planning of diabetic retinopathy. Despite the high-resolution of photographs in fundus photography, the contrast between the blood vessels and the retinal background tends to be poor. Materials and Methods: In this proposed method, contrast-limited adaptive histogram equalization is used for noise cancellation and improving the local contrast of the image. By uniform distribution of gray values, it enhances the image and makes the hidden features more visible. The extraction of the retinal blood vessel depends on two levels of optimization. The first level is the extraction of blood vessels from the retinal image using Kirsch's templates. The second level is used to find the coarse vessels with the assistance of the unsupervised method of Fuzzy C-Means clustering. After segmentation, to remove the optic disc, the region-based active contour method is used. The proposed system is evaluated using DRIVE dataset with 40 images. Results: The performance of the proposed approach is comparable with state of the art techniques. The proposed technique outperforms the existing techniques by achieving an accuracy of 99.55%, sensitivity of 71.83%, and specificity of 99.86% in the experimental setup. Conclusion: The results show that this approach is a suitable alternative technique for the supervised method and it is support for similar fundus images dataset.