Dynamic B-snake model for complex objects segmentation

Abstract

A close-form B-Snake model using statistics information for 2D objects segmentation is presented in this paper. We called it Dynamic B-Snake Model (DBM). It is able to model the features of the object in training set and guide the B-Snake in the deforming procedure. Compared to other deformable models, the DBM maintains the smoothness of curve while still remain compact representation. Moreover, a method of Minimum Mean Square Error (MMSE) is developed to iteratively estimate the position of those control points in the B-Snake model. As it deforms the segments of the B-Spline at a time, instead of individual points, it is very robust against local minima. Furthermore, in order to use available statistical information about the desired object shape, the Principal Component Analysis (PCA) is applied to model the distribution of knot points of training samples. This allows the deformation of B-Snake to synthesize the shape similar to those in the training set. By applying the proposed B-Snake model to medical images, it is shown that our method is more robust and accurate in comparing with the traditional Snake and Active Shape Model(ASM).