Segmentation of MR images using curve evolution and prior information

Segmentation of anatomic structures of the human brain from MR images is important for assessing treatment efficacy, screening for anomalies, and improving our understanding of human development. The labor intensive nature of manual segmentation, however, makes such a technique viable only in selected cases. In this paper we present a new approach to segmentation that involves only minimal human interactions. The technique utilizes a variational formulation to obtain an edge-strength function over the region of interest, and uses curve evolution and a pre-segmented atlas to guide the actual segmentation process. The approach is demonstrated via both phantoms and actual MR images, and when applied to the lateral ventricles and caudate nucleus, showed a size accuracy error of 5%-20% with respect to manual segmentation, depending on the manual segmentation method utilized.