BAT algorithm inspired retinal blood vessel segmentation. Issue 11 (1st November 2018)

• Record Type: Journal Article
• Authors: Sathananthavathi, Vallikutti; Indumathi, Ganesan
• Is Part Of: IET image processing. Volume 12:Issue 11(2018)
• Page Start: 2075
• Page End: 2083
• Publication Date: 2018-11-01
• Subjects: feature extraction -- feature selection -- image classification -- learning (artificial intelligence) -- image segmentation -- biomedical optical imaging -- medical image processing -- diseases -- blood vessels -- optimisation -- eye; digital retinal images -- classifier error minimisation -- feature set -- morphological feature extraction -- phase feature extraction -- local feature extraction -- feature vector -- random forest classifier -- retinal diseases -- medical analysis -- automated retinal blood vessel extraction -- BAT algorithm inspired retinal blood vessel segmentation; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2017.1266
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : The automated extraction of retinal blood vessels is the course of action in the medical analysis of retinal diseases. The proposed methodology for the retinal vessel segmentation is based on BAT algorithm and random forest classifier. A feature vector of 40‐dimensional including local, phase and morphological features is extracted and the feature set which minimises the classifier error is identified by BAT algorithm. The selected features are also identified as the dominant features in the classification. Performance of the proposed method is analysed by the publicly available databases such as digital retinal images for vessel extraction and structured analysis of the retina. The authors' proposed method is highly sensitive to identify the blood vessels, in view of the fact that it corresponds to the ability of the method to identify the blood vessels correctly. BAT algorithm‐based proposed method achieves very high sensitivity and accuracy of about 82.85 and 95.34%, respectively.