Segmentation through a local and adaptive weighting scheme, for contour-based blending of image and prior information
Active Contour Models have been widely used in computer vision for segmentation purposes, while anatomically constrained ACMs have offered a valuable solution on medical image segmentation, specifically for structures with weak boundaries. Efforts have been devoted on various ways of modeling prior knowledge, in terms of the morphology of the structures under investigation. This paper focuses on how to efficiently incorporate prior knowledge, into an ACM evolution framework, using the structures’ distribution map as a second feature image, and blending the two images through a novel adaptive local weighting scheme. For proof of concept the method is applied on hippocampus segmentation in T1-MR brain images, a very challenging task, due to its multivariate surrounding region and the weak, even missing boundaries.