Information-Based cost function for a bayesian MRI segmentation framework

Abstract

A new information-based cost function is introduced for learning the conditional class probability model required in the probabilistic atlas-based brain magnetic resonance image segmentation. Aiming to improve the segmentation results, the α-order Renyi’s entropy is considered as the function to be maximized since this kind of functions has been proved to lead to more discriminative distributions. Additionally, we developed the model parameter update for the considered function, leading to a set of weighted averages dependant on the α factor. Our proposal is tested by segmenting the well-known BrainWeb synthetic brain MRI database and compared against the log-likelihood function. Achieved results show an improvement in the segmentation accuracy of ~ 5% with respect to the baseline cost function.