Segmentation of 3D brain MR using an adaptive K-means clustering algorithm
Mengbei Yan, Joel S. Karp · 2002
An algorithm is described for segmenting 3D MR brain image into K different tissue types, which include gray, white matter and CSF, and maybe other abnormal tissues. MR images considered can be either scale- or multi-valued. Each scale-valued image is modeled as a collection of regions with slowly varying intensity plus a white Gaussian noise. Each tissue type is modeled by a Markov random field with the second order neighborhood in a 3D lattice. The proposed algorithm is an adaptive K-means clustering algorithm for 3-dimensional and multi-valued images. Each iteration consists of two steps: estimate mean intensity at each location for each type, and estimate tissue types by maximizing the a posteriori probability. The algorithm slowly adapts to the local intensity variation of each region, so it is robust to the "shading" effect. It has the potential to routinely process clinical MR images with minimal user involvement. Its performance is tested using patient data.>