Contrast normalization steps for increased sensitivity of a retinal image segmentation method

• Published in: Signal, image and video processing Vol. 11; no. 8; pp. 1509 - 1517
• Main Authors: Soomro, Toufique Ahmed, Khan, Mohammad A. U., Gao, Junbin, Khan, Tariq M., Paul, Manoranjan
• Format: Journal Article
• Language: English
• Published: London Springer London 01.11.2017; Springer Nature B.V
• Subjects: Automation; Computer Imaging; Computer Science; Eye diseases; Filtration; Image contrast; Image Processing and Computer Vision; Image segmentation; Multimedia Information Systems; Original Paper; Pattern Recognition and Graphics; Principal components analysis; Sensitivity analysis; Signal,Image and Speech Processing; Vision; Retinal images; Scale normalization; Hysteresis threshold; PCA
• ISSN: 1863-1703; 1863-1711
• DOI: 10.1007/s11760-017-1114-7

Retinal vessel segmentation plays a major role in the detection of many eye diseases, and it is required to implement an automated algorithm for analyzing the progress of eye diseases. A variety of automated segmentation methods have been presented but almost all studies to date showed weakness in their low sensitivity toward narrow low-contrast vessels. A new segmentation method is proposed to address the issue of low sensitivity, by including modules such as principal component analysis-based color-to-gray conversion, scale normalization factors for improved narrow vessel detection, anisotropic diffusion filtering with an adequate stopping rule, and edge pixel-based hysteresis threshold. The impact of these additional steps is assessed on publicly available databases like DRIVE and STARE. For the case of DRIVE database, the sensitivity is raised from 73 to 75%, while maintaining the accuracy of 96.5%, and found to provide evidence of improved sensitivity.