A modified possibilistic fuzzy c-means clustering algorithm for bias field estimation and segmentation of brain MR image

作者：Ji Ze Xuan; Sun Quan Sen^*; Xia De Shen

来源：Computerized Medical Imaging and Graphics, 2011, 35(5): 383-397.

DOI：10.1016/j.compmedimag.2010.12.001

• Brain MR image segmentation
• Intensity inhomogeneity
• Possibilistic fuzzy c-means algorithm
• Anisotropic weight
• Coherent local and global intensity

A modified possibilistic fuzzy c-means clustering algorithm is presented for fuzzy segmentation of magnetic resonance (MR) images that have been corrupted by intensity inhomogeneities and noise. By introducing a novel adaptive method to compute the weights of local spatial in the objective function, the new adaptive fuzzy clustering algorithm is capable of utilizing local contextual information to impose local spatial continuity, thus allowing the suppression of noise and helping to resolve classification ambiguity. To estimate the intensity inhomogeneity, the global intensity is introduced into the coherent local intensity clustering algorithm and takes the local and global intensity information into account. The segmentation target therefore is driven by two forces to smooth the derived optimal bias field and improve the accuracy of the segmentation task. The proposed method has been successfully applied to 3T, 7T, synthetic and real MR images with desirable results. Comparisons with other approaches demonstrate the superior performance of the proposed algorithm. Moreover, the proposed algorithm is robust to initialization, thereby allowing fully automatic applications.

• 出版日期2011-7
• 单位南京理工大学