Retinal blood vessel segmentation from fundus image using an efficient multiscale directional representation technique Bendlets

Kushol R., Hasanul Kabir M., Abdullah-Al-Wadud M., Islam M. S.

Mathematical Biosciences and Engineering, vol.17, no.6, pp.7751-7771, 2020 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 17 Issue: 6
  • Publication Date: 2020
  • Doi Number: 10.3934/mbe.2020394
  • Journal Name: Mathematical Biosciences and Engineering
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, EMBASE, MathSciNet, MEDLINE, zbMATH
  • Page Numbers: pp.7751-7771
  • Keywords: Bendlets, retinal blood vessel, ensemble classifier, medical image segmentation, contrast enhancement, multi-resolution, multi-scale transform
  • TED University Affiliated: No


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.