Generalized fuzzy clustering for segmentation of multi-spectral magnetic resonance images

作者：He Renjie^*; Datta Sushmita; Sajja Balasrinivasa Rao; Narayana Ponnada A

来源：Computerized Medical Imaging and Graphics, 2008, 32(5): 353-366.

DOI：10.1016/j.conipmediniag.2008.02.002

• adaptive FCM
• contextual constraints
• G-K algorithm
• inhomogeneity field
• multi-spectral segmentation
• MRI
• similairity measures

An integrated approach for multi-spectral segmentation of MR images is presented. This method is based on the fuzzy c-means (FCM) and includes bias field correction and contextual constraints over spatial intensity distribution and accounts for the non-spherical cluster's shape in the feature space. The bias field is modeled as a linear combination of smooth polynomial basis functions for fast computation in the clustering iterations. Regularization terms for the neighborhood continuity of intensity are added into the FCM cost functions. To reduce the computational complexity, the contextual regularizations are separated from the clustering iterations. Since the feature space is not isotropic, distance measure adopted in Gustafson-Kessel (G-K) algorithm is used instead of the Euclidean distance, to account for the non-spherical shape of the clusters in the feature space. These algorithms are quantitatively evaluated on MR brain images using the similarity measures.

• 出版日期2008-7