A novel multi-stage 3d medical image segmentation: methodology and validation

  • Authors:

  • Jianfeng Xu;Lixu Gu;Xiahai Zhuang;Terry Peters

  • Affiliations:

  • Computer Science, Shanghai Jiaotong University, Shanghai, China;Computer Science, Shanghai Jiaotong University, Shanghai, China;Computer Science, Shanghai Jiaotong University, Shanghai, China;Robarts Research Institute, London, Ontario, Canada

  • Venue:
  • CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
  • Year:
  • 2005

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Abstract

In this paper, we present a novel multi-stage algorithm for 3D medical image segmentation that is inspired by an improved Fast Marching method and a morphological reconstruction algorithm. The segmentation procedure consists of three steps: Connectivity Reduction, Hybrid segmentation, and Region recovery. The approach is tested on CT cardiac and MRI brain images, to demonstrate the effectiveness and accuracy of the technique. In order to validate this segmentation algorithm, a novel Radial Distance Based Validation (RDBV) method is proposed that provides a global accuracy (GA) measure. GA is calculated based on Local Radial Distance Errors (LRDE), where measured errors are along radii emitted from points along the skeleton of the object rather than the centroid, in order to accommodate more complicated organ structures. Using this GA measure, our results demonstrate that this multi-stage segmentation is fast and accurate, achieving approximately the same segmentation result as the watershed method, but with a processing speed of 3-5 times faster.