Spatial information of fuzzy clustering based mean best artificial bee colony algorithm for phantom brain image segmentation

Waleed Alomoush (Imam Abdulrahman Bin Faisal University)
Ayat Alrosan (Imam Abdulrahman Bin Faisal University)
Ammar Almomani (Al-Balqa Applied University)
Khalid Alissa (Imam Abdulrahman bin Faisal University)
Osama A. Khashan (Saudi Electronic University)
Ahmad Al-nawasrah (British university of Bahrain)

International Journal of Electrical and Computer Engineering
Vol 11, No 5: October 2021

Fuzzy c-means algorithm (FCM) is among the most commonly used in the medical image segmentation process. Nevertheless, the traditional FCM clustering approach has been several weaknesses such as noise sensitivity and stuck in local optimum, due to FCM hasn’t able to consider the information of contextual. To solve FCM problems, this paper presented spatial information of fuzzy clustering-based mean best artificial bee colony algorithm, which is called SFCM-MeanABC. This proposed approach is used contextual information in the spatial fuzzy clustering algorithm to reduce sensitivity to noise and its used MeanABC capability of balancing between exploration and exploitation that is explore the positive and negative directions in search space to find the best solutions, which leads to avoiding stuck in a local optimum. The experiments are carried out on two kinds of brain images the Phantom MRI brain image with a different level of noise and simulated image. The performance of the SFCM-MeanABC approach shows promising results compared with SFCM-ABC and other stats of the arts.