On Kernel Based Rough Intuitionistic Fuzzy C-means Algorithm and a Comparative Analysis

2014 ◽  
pp. 349-359 ◽  
Author(s):  
B. K. Tripathy ◽  
Anurag Tripathy ◽  
K. Govindarajulu ◽  
Rohan Bhargav
Keyword(s):  
Comparative Analysis ◽  
Fuzzy C Means ◽  
Intuitionistic Fuzzy ◽  
Fuzzy C Means Algorithm

Rough intuitionistic fuzzy C-means algorithm and a comparative analysis

2013 ◽  
Author(s):  
Rohan Bhargava ◽  
B. K. Tripathy ◽  
Anurag Tripathy ◽  
Rajkamal Dhull ◽  
Ekta Verma ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Fuzzy C Means ◽  
Intuitionistic Fuzzy ◽  
Fuzzy C Means Algorithm

scholarly journals Intuitionistic Fuzzy C-Means Algorithm Based on Membership Information Transfer-Ring and Similarity Measurement

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 696
Author(s):  
Haipeng Chen ◽  
Zeyu Xie ◽  
Yongping Huang ◽  
Di Gai
Keyword(s):  
Brain Tumor ◽  
Information Transfer ◽  
Magnetic Resonance Images ◽  
Low Noise ◽  
Similarity Measurement ◽  
Noise Sensitivity ◽  
Fuzzy C Means ◽  
Intuitionistic Fuzzy ◽  
Membership Information ◽  
Fuzzy C Means Algorithm

The fuzzy C-means clustering (FCM) algorithm is used widely in medical image segmentation and suitable for segmenting brain tumors. Therefore, an intuitionistic fuzzy C-means algorithm based on membership information transferring and similarity measurements (IFCM-MS) is proposed to segment brain tumor magnetic resonance images (MRI) in this paper. The original FCM lacks spatial information, which leads to a high noise sensitivity. To address this issue, the membership information transfer model is adopted to the IFCM-MS. Specifically, neighborhood information and the similarity of adjacent iterations are incorporated into the clustering process. Besides, FCM uses simple distance measurements to calculate the membership degree, which causes an unsatisfactory result. So, a similarity measurement method is designed in the IFCM-MS to improve the membership calculation, in which gray information and distance information are fused adaptively. In addition, the complex structure of the brain results in MRIs with uncertainty boundary tissues. To overcome this problem, an intuitive fuzzy attribute is embedded into the IFCM-MS. Experiments performed on real brain tumor images demonstrate that our IFCM-MS has low noise sensitivity and high segmentation accuracy.

A modified intuitionistic fuzzy c-means algorithm incorporating hesitation degree

2019 ◽  
Vol 122 ◽  
pp. 45-52 ◽  
Author(s):  
Hanuman Verma ◽  
Akshansh Gupta ◽  
Dhirendra Kumar
Keyword(s):  
Fuzzy C Means ◽  
Intuitionistic Fuzzy ◽  
Hesitation Degree ◽  
Fuzzy C Means Algorithm

Color-Based Image Segmentation by Means of a Robust Intuitionistic Fuzzy C-means Algorithm

2020 ◽  
Vol 22 (3) ◽  
pp. 901-916
Author(s):  
Dante Mújica-Vargas ◽  
Jean Marie Vianney Kinani ◽  
José de Jesús Rubio
Keyword(s):  
Image Segmentation ◽  
Fuzzy C Means ◽  
Intuitionistic Fuzzy ◽  
Fuzzy C Means Algorithm
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