Medical image segmentation by hybridizing ant colony optimization and fuzzy clustering algorithm

2011 ◽  
Author(s):  
Jeongmin Yu ◽  
Sang-Goog Lee ◽  
Moongu Jeon
Keyword(s):  
Image Segmentation ◽  
Ant Colony Optimization ◽  
Fuzzy Clustering ◽  
Medical Image ◽  
Clustering Algorithm ◽  
Medical Image Segmentation ◽  
Ant Colony ◽  
Fuzzy Clustering Algorithm

scholarly journals A Modified Intuitionistic Fuzzy Clustering Algorithm for Medical Image Segmentation

2018 ◽  
Vol 27 (4) ◽  
pp. 593-607 ◽  
Author(s):  
S.V. Aruna Kumar ◽  
B.S. Harish
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Medical Image ◽  
Clustering Algorithm ◽  
Intuitionistic Fuzzy Set ◽  
Medical Image Segmentation ◽  
Cluster Center ◽  
Intuitionistic Fuzzy ◽  
Elementary Condition ◽  
Intuitionistic Fuzzy Clustering

Abstract This paper presents a modified intuitionistic fuzzy clustering (IFCM) algorithm for medical image segmentation. IFCM is a variant of the conventional fuzzy C-means (FCM) based on intuitionistic fuzzy set (IFS) theory. Unlike FCM, IFCM considers both membership and nonmembership values. The existing IFCM method uses Sugeno’s and Yager’s IFS generators to compute nonmembership value. But for certain parameters, IFS constructed using above complement generators does not satisfy the elementary condition of intuitionism. To overcome this problem, this paper adopts a new IFS generator. Further, Hausdorff distance is used as distance metric to calculate the distance between cluster center and pixel. Extensive experimentations are carried out on standard datasets like brain, lungs, liver and breast images. This paper compares the proposed method with other IFS based methods. The proposed algorithm satisfies the elementary condition of intuitionism. Further, this algorithm outperforms other methods with the use of various cluster validity functions.

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