Emary E, Zawbaa H, Hassanien AE, Schaefer G, Azar AT (2014). Retinal Blood Vessel Segmentation using Bee Colony Optimization and Pattern Search. IEEE 2014 International Joint Conference on Neural Networks (IJCNN 2014), July 6-11, Beijing International Convention Center, Beijing, China
|Authors:||Eid Emary, Hossam M. Zawbaa, Aboul Ella Hassanien, Gerald Schaefer, Ahmad Taher Azar|
|Full paper||Not Available|
|Supplementary materials||Not Available|
Accurate segmentation of retinal blood vessels is an important task in computer aided diagnosis of retinopathy. In this paper, we propose an automated retinal blood vessel segmentation approach based on artificial bee colony optimisation in conjunction with fuzzy c-means clustering. Artificial bee colony optimisation is applied as a global search method to find cluster centers of the fuzzy c-means objective function. Vessels with small diameters appear distorted and hence cannot be correctly segmented at the first segmentation level due to confusion with nearby pixels. We employ a pattern search approach to optimisation in order to localise small vessels with a different fitness function. The proposed algorithm is tested on the publicly available DRIVE and STARE retinal image databases and confirmed to deliver performance that is comparable with state-of-the-art techniques in terms of accuracy, sensitivity and specificity.