Review: A comparative study of deformable contour methods on medical image segmentation

  • Authors:

  • Lei He;Zhigang Peng;Bryan Everding;Xun Wang;Chia Y. Han;Kenneth L. Weiss;William G. Wee

  • Affiliations:

  • Information Technology Department, Armstrong Atlantic State University, 11935 Abercorn Street, Savannah, GA 31419, USA;Electrical & Computer Engineering and Computer Science Department, University of Cincinnati, Cincinnati, OH 45221-0030, USA;Electrical & Computer Engineering and Computer Science Department, University of Cincinnati, Cincinnati, OH 45221-0030, USA;Electrical & Computer Engineering and Computer Science Department, University of Cincinnati, Cincinnati, OH 45221-0030, USA;Electrical & Computer Engineering and Computer Science Department, University of Cincinnati, Cincinnati, OH 45221-0030, USA;Department of Psychiatry, University of Cincinnati, Cincinnati, OH 45267-0559, USA;Electrical & Computer Engineering and Computer Science Department, University of Cincinnati, Cincinnati, OH 45221-0030, USA

  • Venue:
  • Image and Vision Computing
  • Year:
  • 2008

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Abstract

A comparative study to review eight different deformable contour methods (DCMs) of snakes and level set methods applied to the medical image segmentation is presented. These DCMs are now applied extensively in industrial and medical image applications. The segmentation task that is required for biomedical applications is usually not simple. Critical issues for any practical application of DCMs include complex procedures, multiple parameter selection, and sensitive initial contour location. Guidance on the usage of these methods will be helpful for users, especially those unfamiliar with DCMs, to select suitable approaches in different conditions. This study is to provide such guidance by addressing the critical considerations on a common image test set. The test set of selected images offers different and typical difficult problems encountered in biomedical image segmentation. The studied DCMs are compared using both qualitative and quantitative measures and the comparative results highlight both the strengths and limitations of these methods. The lessons learned from this medical segmentation comparison can also be translated to other image segmentation domains.