access icon free Retinal blood vessel segmentation using the elite-guided multi-objective artificial bee colony algorithm

© The Institution of Engineering and Technology
Received 01/08/2018, Accepted 06/08/2018, Revised 01/05/2018, Published 14/08/2018

Retinal vessel segmentation constitutes an essential part of computer-assisted tools for the diagnosis of ocular diseases. In this study, the authors propose an unsupervised retinal blood vessels segmentation approach based on the elite-guided multi-objective artificial bee colony (EMOABC) algorithm. The proposed method exploits several criteria simultaneously to improve the accuracy of the segmentation results. An energy curve function is used to calculate the values of the thresholding criteria, in order to reduce the noise response from lesions and select the optimal thresholds that separate the blood vessels from the background. In order to achieve computational speed up, a stopping criterion method is used to adjust the parameters of the EMOABC algorithm. The proposed method is computationally simple and faster than most of the available unsupervised algorithms, demonstrating fast convergence to the final segmentation. Additionally, the proposed vessel segmentation method outperforms the metaheuristics vessels segmentation algorithms reported in the literature. The achieved mean discrepancy metrics for the proposed approach are 94.5% accuracy, 97.4% specificity and 73.9% sensitivity for DRIVE database, and 94% accuracy, 96.2% specificity and 73.7% sensitivity for STARE database.

Inspec keywords: optimisation; image enhancement; image segmentation; diseases; biomedical optical imaging; eye; ant colony optimisation; blood vessels; medical image processing

Other keywords: retinal blood vessel segmentation; ocular diseases; EMOABC algorithm; elite-guided multiobjective artificial bee colony algorithm; final segmentation; segmentation results; energy curve function; optimal thresholds; essential part; vessel segmentation method; metaheuristics vessels segmentation algorithms; available unsupervised algorithms; computational speed; computer-assisted tools; retinal vessel segmentation; thresholding criteria; unsupervised retinal blood vessels segmentation approach

Subjects: Optimisation techniques; Biomedical measurement and imaging; Patient diagnostic methods and instrumentation; Biology and medical computing; Optical, image and video signal processing; Computer vision and image processing techniques; Optimisation techniques

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