Gain field correction fast fuzzy c-means algorithm for segmenting magnetic resonance images

Authors:
Jingjing Song;Qingjie Zhao;Yuanquan Wang;Jie Tian
Affiliations:
Dept. of Computer Science & Engineering, Beijing Institute of Technology, Beijing, China;Dept. of Computer Science & Engineering, Beijing Institute of Technology, Beijing, China;Dept. of Computer Science & Engineering, Beijing Institute of Technology, Beijing, China;Institute of Automation, Chinese Academy of Sciences, Beijing, P.R. China
Venue:
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Year:
2006

Citing 2
Cited 0

An adaptive fuzzy C-means algorithm for image segmentation in the presence of intensity inhomogeneities

Pattern Recognition Letters
A Modified Fuzzy C-Means Algorithm for Segmentation of MRI

ICCIMA '03 Proceedings of the 5th International Conference on Computational Intelligence and Multimedia Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we present a new and fast algorithm of fuzzy segmentation for MR image, which is corrupted by the intensity inhomogeneity. The algorithm is formulated by modifying the FFCM algorithm to incorporate a gain field, which compensate for such inhomogeneities. In each iteration, we allow the gain field transforming to a gain field image and filter it using an iterative low-pass filter, and then revert the gain field image to gain field term again for the next iteration. We also use c-means algorithm initializing the centroids to further accelerate our algorithm. Our method reduces lots of executive time and will obtain a high-quality result. The efficiency of the algorithm is demonstrated on different magnetic resonance images.