A fractional filter based efficient algorithm for retinal blood vessel segmentation. (May 2020)

• Record Type: Journal Article
• Authors: Shukla, Anil K.; Pandey, Rajesh K.; Pachori, Ram Bilas
• Is Part Of: Biomedical signal processing and control. Volume 59(2020)
• Publication Date: 2020-05
• Subjects: Fractional derivatives -- Image segmentation -- Principal component analysis -- Retinal blood vessel segmentation; 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.2020.101883
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A new fractional filter is discussed in this paper. An unsupervised algorithm for detection of centreline in the retinal image is introduced. We have introduced an unsupervised algorithm for retinal vessel segmentation that is discussed in this paper. Performance of proposed algorithm is compared with other existing methods. Simulation results show that performance of the proposed method is better than most of the discussed methods. Our approach is computationally efficient and applicable in real time. Abstract: This paper presents a new fractional filter and an algorithm for retinal blood vessel segmentation. The proposed fractional filter is designed with the help of a weighted fractional derivative and an exponential weight factor. We have utilized the fractional filter and the eigenvalue maps of a local covariance matrix to develop the algorithm for retinal vessel segmentation. The local covariance matrix is formed by a second-order image moment. Experiments are performed on two well-studied evaluation databases named STARE and DRIVE. Experimental results show that the proposed method is computationally efficient, and the average accuracy of the vessel segmentation on STARE and DRIVE databases are 95.73% and 94.76%, respectively. The performance of the proposed method is compared with other existing methods. Simulation results show that the average performance of the proposed method is comparatively better than most of the discussed methods.