A modified fuzzy C-means algorithm using scale control spatial information for MRI image segmentation in the presence of noise

2015 ◽  
Vol 29 (9) ◽  
pp. 492-505 ◽  
Author(s):  
Jamuna Kanta Sing ◽  
Sudip Kumar Adhikari ◽  
Dipak Kumar Basu
Keyword(s):  
Image Segmentation ◽  
Spatial Information ◽  
Fuzzy C Means ◽  
Scale Control ◽  
Fuzzy C Means Algorithm ◽  
Mri Image

Type-2 Fuzzy C-Means Algorithm with Spatial Informations and Membership Constrains for Liver Segmentation

2013 ◽  
Vol 746 ◽  
pp. 570-574
Author(s):  
Qin Li Zhang ◽  
Ya Fan Yue ◽  
Zhao Zhuang Guo
Keyword(s):  
Image Segmentation ◽  
Fuzzy Sets ◽  
Fuzzy Set ◽  
Spatial Information ◽  
Target Area ◽  
Fuzzy C Means ◽  
Liver Image ◽  
Fuzzy C Means Algorithm

The Fuzzy C-Means algorithm with spatial informations and membership constrains is a very effective and efficient image segmentation method. However£¬it is founded with Type-1 fuzzy sets, which can not handle the uncertainties existing in liver image well.The type-2 fuzzy sets have better performance on handling uncertainties than Type-1 fuzzy set. In this paper, a new robust Type-2 FCM image segmentation algorithm is proposed aiming to improve the segmentation precision and robustness of liver image by introducing the type-2 fuzzy set into FCM with spatial information and membership constrains. We extend the type-1 fuzzy set of membership to interval type-2 fuzzy set using two fuzzifiers and which create a footprint of uncertainty (FOU). The experimental results show that the target area of the liver in CT images can be segmented well by the proposed method.

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