Multiplicative versus Additive Bias Field Models for Unified Partial-Volume Segmentation and Inhomogeneity Correction in Brain MR Images

2008 ◽  
Author(s):  
Su Wang ◽  
Lihong Li ◽  
Hongbing Lu ◽  
Zhengrong Liang
Keyword(s):  
Bias Field ◽  
Mr Images ◽  
Volume Segmentation ◽  
Partial Volume ◽  
Inhomogeneity Correction ◽  
Brain Mr Images

A unifying framework for partial volume segmentation of brain MR images

2003 ◽  
Vol 22 (1) ◽  
pp. 105-119 ◽  
Author(s):  
K. Van Leemput ◽  
F. Maes ◽  
D. Vandermeulen ◽  
P. Suetens
Keyword(s):  
Mr Images ◽  
Volume Segmentation ◽  
Partial Volume ◽  
Brain Mr Images

A Modified Fuzzy C-Means Algorithm Brain MR Images Segmentation with Bias Field Compensation

2013 ◽  
Vol 756-759 ◽  
pp. 1349-1355 ◽  
Author(s):  
Xiao Li Liu ◽  
Yu Ting Guo ◽  
Jun Kong ◽  
Jian Zhong Wang
Keyword(s):  
Brain Mri ◽  
Bias Field ◽  
Local Information ◽  
Mr Images ◽  
Fcm Algorithm ◽  
Different Types ◽  
Field Estimation ◽  
Non Local ◽  
Brain Mr Images ◽  
Fuzzy C Means Algorithm

Segmentation of brain magnetic resonance (MR) images is always required as a preprocessing stage in many brain analysis tasks. Nevertheless, the bias field (BF, also called intensity in-homogeneities) and noise in the MRI images always make the accurate segmentation difficult. In this paper, we present a modified FCM algorithm for bias field estimation and segmentation of brain MRI. Our method is formulated by modifying the objective function of the standard FCM algorithm. It aims to compensate for bias field and incorporate both the local and non-local information into the distance function to restrain the noise of the image. We have conducted extensive experimental and have compared our method with different types of FCM extension methods using simulated MRI images. The results show that our proposed method can deal with the bias field and noise effectively and outperforms other methods.

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