Gain Field Correction Fast Fuzzy c-Means Algorithm for Segmenting Magnetic Resonance Images

2006 ◽  
pp. 1242-1247
Author(s):  
Jingjing Song ◽  
Qingjie Zhao ◽  
Yuanquan Wang ◽  
Jie Tian
Keyword(s):  
Magnetic Resonance ◽  
Magnetic Resonance Images ◽  
Fuzzy C Means ◽  
Fuzzy C Means Algorithm

scholarly journals APSO based LLRBFNN Model and Enhanced Fuzzy C Means algorithm for Brain Tumor Detection and Classification from Magnetic Resonance Image

2019 ◽  
Vol 8 (4) ◽  
pp. 490
Author(s):  
Satyasis Mishra ◽  
Premananda Sahu ◽  
Manas Ranjan Senapati
Keyword(s):  
Magnetic Resonance ◽  
Malignant Tumors ◽  
Research Work ◽  
Tumor Detection ◽  
Magnetic Resonance Images ◽  
Mr Images ◽  
Fuzzy C Means ◽  
Comparison Results ◽  
Accelerated Particle Swarm Optimization ◽  
Fuzzy C Means Algorithm

This paper presents a novel APSO (Accelerated Particle Swarm Optimization) Predicated LLRBFNN (Local Linear Radial Basis Function Neural Network) model for automatic encephalon tumor detection and classification. The enhanced fuzzy c means algorithm (EnFCM) has been proposed for image segmentation and the GLCM (Gray Level Co-occurrence Matrix) technique for feature extraction from MR (Magnetic Resonance) images. This research work aims to utilize the hybrid models and algorithms for relegation and segmentation of encephalon tumors from the MR images. The extracted features have been alimented as input to the proposed APSO predicated LLRBFNN model for relegation of benign and malignant tumors. In this research work the proposed LLRBFNN model weights are optimized by utilizing APSO training which will provide unique solution to mitigation the hectic task of radiologist from manual detection of encephalon tumors from MR Images. Additionally the centers of the LLRBFNN model are culled by the Enhanced Fuzzy C Means algorithm and updated by the APSO algorithm. The results of proposed PSO predicated LLRBFNN model has been compared with PSO-LLRBFNN model, APSO-RBFNN and PSO-RBFNN model and the comparison results are presented. The experimental results obtained from the proposed model shows better relegation results as compared to the subsisting models proposed anteriorly.  

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.

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