MAP segmentation of magnetic resonance images using mean field annealing
Ambalavaner Logenthiran, Wesley E. Snyder, Peter Santago, Kerry M. Link · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1991
An algorithm is described which segments magnetic resonance images while removing the noise from the images without blurring or other distortion of edges. The problem of segmentation and noise removal is posed as a restoration of an uncorrupted image, given additive white Gaussian noise and a segmentation cost. The problem is solved using a strategy called Mean Field Annealing. An a priori statistical model of the image, which includes the region classification, is chosen which drives the minimization toward solutions which are locally homogeneous and globally classified. Application of the algorithm to brain and knee images is presented.