›› 2016, Vol. 31 ›› Issue (3): 501-511.doi: 10.1007/s11390-016-1643-5

Special Issue: Artificial Intelligence and Pattern Recognition; Computer Graphics and Multimedia

• Special Section of CVM 2016 • Previous Articles     Next Articles

A Modified Fuzzy C-Means Algorithm for Brain MR Image Segmentation and Bias Field Correction

Wen-Qian Deng(邓雯倩)1, Xue-Mei Li(李雪梅)1,*, Xifeng Gao(高希峰)2, and Cai-Ming Zhang(张彩明)1   

  1. 1 School of Computer Science and Technology, Shandong University, Jinan 250101, China;
    2 Department of Computer Science, University of Houston, Houston, TX 77004, U.S.A.
  • Received:2015-12-02 Revised:2016-03-15 Online:2016-05-05 Published:2016-05-05
  • Contact: Xue-Mei Li E-mail:xmli@sdu.edu.cn
  • Supported by:

    This work was supported by the National Natural Science Foundation of China under Grant Nos. 61332015, 61373078, 61572292, and 61272430, and the National Research Foundation for the Doctoral Program of Higher Education of China under Grant No. 20110131130004.

In quantitative brain image analysis, accurate brain tissue segmentation from brain magnetic resonance image (MRI) is a critical step. It is considered to be the most important and difficult issue in the field of medical image processing. The quality of MR images is influenced by partial volume effect, noise, and intensity inhomogeneity, which render the segmentation task extremely challenging. We present a novel fuzzy c-means algorithm (RCLFCM) for segmentation and bias field correction of brain MR images. We employ a new gray-difference coefficient and design a new impact factor to measure the effect of neighbor pixels, so that the robustness of anti-noise can be enhanced. Moreover, we redefine the objective function of FCM (fuzzy c-means) by adding the bias field estimation model to overcome the intensity inhomogeneity in the image and segment the brain MR images simultaneously. We also construct a new spatial function by combining pixel gray value dissimilarity with its membership, and make full use of the space information between pixels to update the membership. Compared with other state-of-the-art approaches by using similarity accuracy on synthetic MR images with different levels of noise and intensity inhomogeneity, the proposed algorithm generates the results with high accuracy and robustness to noise.

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