Blood vessel detection based on fractional Hessian matrix with non-singular Mittag–Leffler Gaussian kernel. (September 2019)

• Record Type: Journal Article
• Authors: Solís-Pérez, J.E.; Gómez-Aguilar, J.F.; Escobar-Jiménez, R.F.; Reyes-Reyes, J.
• Is Part Of: Biomedical signal processing and control. Volume 54(2019)
• Publication Date: 2019-09
• Subjects: Fractional Gaussian kernel -- Blood vessel -- Hessian matrix -- Non-singular kernel -- Atangana–Baleanu fractional derivative -- Adams–Bashforth–Moulton scheme; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2019.101584
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: We proposed a novel method based on fractional Gaussian kernel involving the Atangana–Baleanu fractional derivative to detect blood vessels in retinal images. The non-integer orders of the fractional Gaussian mask and the fractional Gaussian matrix were optimized by Cuckoo Search algorithm. Manual segmentation was consider to evaluate the effectiveness and the robustness of our methodology. Abstract: In this work, we proposed a novel method based on fractional Gaussian kernel involving the Atangana–Baleanu fractional derivative to detect blood vessels in retinal images. This method is based on the fractional Gaussian matrix eigenvalues analysis to obtain the principal curvature. The non-integer orders of the fractional Gaussian mask for retinal image denoising and the fractional Gaussian matrix were optimized by Cuckoo Search algorithm to get more accurate results. To evaluate the effectiveness and the robustness of the method it was compared with manual segmentation and other works reported in the literature.