GC-ASM: Synergistic integration of graph-cut and active shape model strategies for medical image segmentation

  • Authors:

  • Xinjian Chen;Jayaram K. Udupa;Abass Alavi;Drew A. Torigian

  • Affiliations:

  • School of Electronics and Information Engineering, Soochow University, Suzhou 215006, China;Medical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, United States;Hospital of the University of Pennsylvania, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104-6021, United States;Hospital of the University of Pennsylvania, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104-6021, United States

  • Venue:
  • Computer Vision and Image Understanding
  • Year:
  • 2013

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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) method with the model based ASM method to arrive at the GC-ASM method for medical image segmentation. A multi-object GC cost function is proposed which effectively integrates the ASM shape information into the GC framework. The proposed method consists of two phases: model building and segmentation. In the model building phase, the ASM model is built and the parameters of the GC are estimated. The segmentation phase consists of two main steps: initialization (recognition) and delineation. For initialization, an automatic method is proposed which estimates the pose (translation, orientation, and scale) of the model, and obtains a rough segmentation result which also provides the shape information for the GC method. For delineation, an iterative GC-ASM algorithm is proposed which performs finer delineation based on the initialization results. The proposed methods are implemented to operate on 2D images and evaluated on clinical chest CT, abdominal CT, and foot MRI data sets. The results show the following: (a) An overall delineation accuracy of TPVF96%, FPVF