Multi-objective retinal vessel localization using flower pollination search algorithm with pattern search

This paper presents a multi-objective retinal blood vessels localization approach based on flower pollination search algorithm (FPSA) and pattern search (PS) algorithm. FPSA is a new evolutionary algorithm based on the flower pollination process of flowering plants. The proposed multi-objective fitness function uses the flower pollination search algorithm (FPSA) that searches for the optimal clustering of the given retinal image into compact clusters under some constraints. Pattern search (PS) as local search method is then applied to further enhance the segmentation results using another objective function based on shape features. The proposed approach for retinal blood vessels localization is applied on public database namely DRIVE data set. Results demonstrate that the performance of the proposed approach is comparable with state of the art techniques in terms of accuracy, sensitivity, and specificity with many extendable features.