Open AccessSpatial information of fuzzy clustering based mean best artificial bee colony algorithm for phantom brain image segmentation
Author(s) -
Waleed Alomoush,
Ayat Alrosan,
Ammar Almomani,
Khalid Alissa,
Osama A. Khashan,
Ahmad Al-Nawasrah
Publication year - 2021
Publication title -
international journal of power electronics and drive systems/international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
eISSN - 2722-2578
pISSN - 2722-256X
DOI - 10.11591/ijece.v11i5.pp4050-4058
Subject(s) - cluster analysis , computer science , artificial intelligence , fuzzy logic , image segmentation , sensitivity (control systems) , noise (video) , imaging phantom , spatial analysis , segmentation , pattern recognition (psychology) , fuzzy clustering , algorithm , image (mathematics) , mathematics , statistics , medicine , electronic engineering , engineering , radiology
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.