Multilevel thresholding selection based on the artificial bee colony algorithm for image segmentation

2011 ◽  
Author(s):  
Ming-Huwi Horng
Keyword(s):  
Image Segmentation ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Multilevel Thresholding ◽  
Bee Colony

Noisy Image Segmentation Based on Artificial Bee Colony Algorithm

2014 ◽  
Vol 687-691 ◽  
pp. 3652-3655
Author(s):  
Yong Hao Xiao ◽  
Zhuo Bin He ◽  
Yao Hu ◽  
Wei Yu Yu
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Experimental Results ◽  
New Method ◽  
Noisy Image ◽  
Noisy Images ◽  
Bee Colony

Segmentation of noisy images is one of the most challenging problems in image analysis. It hasn’t yet been solved very well. In this paper, we propose a new method for image segmentation, which is able to segment two kinds of noisy images. The experimental results prove that Artificial Bee Colony Algorithm performs better for two types of noisy images.

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

2021 ◽  
Vol 11 (5) ◽  
pp. 4050
Author(s):  
Waleed Alomoush ◽  
Ayat Alrosan ◽  
Ammar Almomani ◽  
Khalid Alissa ◽  
Osama A. Khashan ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Clustering Algorithm ◽  
Spatial Information ◽  
Medical Image Segmentation ◽  
Local Optimum ◽  
Brain Image ◽  
Bee Colony

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.

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