Emary E, Zawbaa H, Hassanien AE, Schaefer G, Azar AT (2014). Retinal Vessel Segmentation based on Possibilistic Fuzzy c-means Clustering Optimised with Cuckoo 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|
Automated analysis of retinal vessels is essential for the diagnosis of a wide range of eye diseases and plays an important role in automatic retinal disease screening systems. In this paper, we present an approach to automatic vessel segmentation in retinal images that utilises possibilistic fuzzy c-means (PFCM) clustering to overcome the problems of the conventional fuzzy c-means objective function. In order to obtain optimised clustering results using PFCM, a cuckoo search method is used. The cuckoo search algorithm, which is based on the brood parasitic behaviour of some cuckoo species in combination with the Levy flight behaviour of some birds and fruit flies, is applied to drive the optimisation of the fuzzy clustering. The performance of our algorithm is analysed on two benchmark databases, the DRIVE and STARE datasets, and encouraging segmentation performance is observed.