Open AccessRetinal blood vessel segmentation from fundus image using an efficient multiscale directional representation technique Bendlets
Author(s) -
Rafsanjany Kushol,
Md. Hasanul Kabir,
M. Abdullah-Al-Wadud,
Saiful Islam
Publication year - 2020
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2020394
Subject(s) - artificial intelligence , computer science , segmentation , fundus (uterus) , computer vision , robustness (evolution) , hypertensive retinopathy , retinal , macular degeneration , diabetic retinopathy , biometrics , benchmark (surveying) , image segmentation , blood vessel , pattern recognition (psychology) , feature (linguistics) , ophthalmology , medicine , biochemistry , chemistry , linguistics , philosophy , geodesy , psychiatry , geography , gene , endocrinology , diabetes mellitus
The improper circulation of blood flow inside the retinal vessel is the primary source of most of the optical disorders including partial vision loss and blindness. Accurate blood vessel segmentation of the retinal image is utilized for biometric identification, computer-assisted laser surgical procedure, automatic screening, and diagnosis of ophthalmologic diseases like Diabetic retinopathy, Age-related macular degeneration, Hypertensive retinopathy, and so on. Proper identification of retinal blood vessels at its early stage assists medical experts to take expedient treatment procedures which could mitigate potential vision loss. This paper presents an efficient retinal blood vessel segmentation approach where a 4-D feature vector is constructed by the outcome of Bendlet transform, which can capture directional information much more efficiently than the traditional wavelets. Afterward, a bunch of ensemble classifiers is applied to find out the best possible result of whether a pixel falls inside a vessel or non-vessel segment. The detailed and comprehensive experiments operated on two benchmark and publicly available retinal color image databases (DRIVE and STARE) prove the effectiveness of the proposed approach where the average accuracy for vessel segmentation accomplished approximately 95%. Furthermore, in comparison with other promising works on the aforementioned databases demonstrates the enhanced performance and robustness of the proposed method.