Cortical gray matter segmentation using an improved watershed-transform

An accurate segmentation of white matter, gray matter and cerebrospinal fluid (CSF) in MR images of the brain is key to understanding important brain diseases. We present a new system for segmentation of MR images of the brain, based on a novel modification of the watershed transform. Our proposed improvement is to substitute the single contour detection function (usually the gradient) of the original watershed transform with a set of functions especially tailored for the detection of each structure in the image. In this paper, these functions are based on a previous probability calculation, using normal distributions and a Markov Random Field. To improve the detection of the sulci, where the partial volume effect often masks the presence of CSF, the probability values for gray matter and CSF are modified using the absolute value of the distance to the white matter and the ridgeness of this distance. We also propose a novel way to initialize the watershed transform by using a probabilistic atlas: in this way, no user interaction is needed. Validation experiments indicate an accurate segmentation of the interesting structures.