A Modified Genetic Algorithm Based FCM Clustering Algorithm for Magnetic Resonance Image Segmentation

2017 ◽  
pp. 435-443 ◽  
Author(s):  
Sunanda Das ◽  
Sourav De
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Magnetic Resonance ◽  
Magnetic Resonance Image ◽  
Clustering Algorithm ◽  
Modified Genetic Algorithm ◽  
Fcm Clustering

Fuzzy Expectation Maximum Algorithm for Magnetic Resonance Image Segmentation

2010 ◽  
Vol 439-440 ◽  
pp. 1618-1623
Author(s):  
Yong Yang
Keyword(s):  
Image Segmentation ◽  
Magnetic Resonance ◽  
Em Algorithm ◽  
Expectation Maximization ◽  
Magnetic Resonance Image ◽  
Clustering Algorithm ◽  
Mr Images ◽  
Mr Image ◽  
Fcm Algorithm ◽  
Mr Image Segmentation

Fuzzy clustering algorithm especially the fuzzy c-means (FCM) algorithm has been widely used for segmentation of brain magnetic resonance (MR) images. However, the conventional FCM algorithm has a very serious shortcoming, i.e., the algorithm tends to balance the number of points in each cluster during the classification. Therefore, when this algorithm is applied to segment the MR images with quite different size of objects, it will lead to bad segmentation. To overcome this problem, a novel fuzzy expectation maximization (FEM) algorithm is presented in this paper. The algorithm is developed by extending the classical hard EM algorithm into soft EM algorithm through integrating the fuzzy and statistical techniques. Compared with the FCM algorithm, the experimental results on MR image segmentation clearly indicate that the proposed FEM algorithm has better performance for the segmentation.

scholarly journals Uterine magnetic resonance image segmentation based on deep learning

2021 ◽  
Vol 1861 (1) ◽  
pp. 012067
Author(s):  
Yu’ang Niu ◽  
Yuanyang Zhang ◽  
Liping Ying ◽  
Hong Li ◽  
Wenbo Chen ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Magnetic Resonance Image
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