GC-ASM: synergistic integration of active shape modeling and graph-cut methods

Abstract

Image segmentation methods may be classified into two categories: purely image based and model based. Each of these two classes has its own advantages and disadvantages. In this paper, we propose a novel synergistic combination of the image based graph-cut (GC) methods with the model based ASM methods to arrive at the GC-ASM method. GC-ASM effectively combines the rich statistical shape information embodied in ASM with the globally optimal delineation capability of the GC method. We propose a new GC cost function, which effectively integrates the specific image information with the ASM shape information. The ASM results are fully utilized to help GC in several ways: (1) For automatically selecting seeds to do GC segmentation, thus helping GC with object recognition; (2) For refining the parameters of the GC algorithm from the ASM result; (3) For bringing object shape information into the GC cost computation. (4) In turn, for using the cost of GC result to improve the ASM's object recognition process. The proposed methods are implemented to operate on 2D images and tested on a clinical abdominal CT data set. The results show: (1) GC-ASM becomes largely independent of initialization. (2) The number of landmarks can be reduced by a factor of 3 in GC-ASM over that in ASM. (3) The accuracy of segmentation via GC-ASM is considerably better than that of ASM.