Cortical brain structures segmentation using constrained optimization and intensity coupling

Brain image segmentation is one of the most important applications in medicine and also is one of the most challenging topics in the field of medical image processing. In general, most automatic segmentation methods consist of an energy function, a shape model, and an optimization strategy. Each plays an important role in the design of an accurate segmentation algorithm. Here we introduce a modified version of a coupled structure segmentation algorithm that is based on earlier paper. Specifically, we have 1) utilized a multiple atlas strategy to estimate a joint probability mass function of the location and tissue type information of the structures; 2) analyzed the relationship among the various structures to achieve more robust probability density function (pdf) estimation; 3) added a constraint to the optimization process to minimize intersection among the different structures; and 4) demonstrated the effectiveness of the method for the segmentation of certain brain structures.