A new deformable model for 3D image segmentation

Abstract

A fully 3D active surface model is presented with self-inflation and self-deflation forces. The model makes full use of 3D image information, deforms locally and allowes strong deformation. The self-inflation and self-deflation forces enable the active surface to travel a long distance without the help from any external forces. We introduce a method of adapting model parameters, which enables our model to bypass some noise and irrelevant edge points. The model is tested with synthetic and real images. Accurate segmentation results are obtained in the presence of image noise and imperfect image data. Importantly, the model is capable of converging to the correct boundary even if the initial estimate is not close. Computational efficiency of segmentation with our model is addressed.