A novel approach for curve evolution in segmentation of medical images

Mahshid Farzinfar, Zhong Xue, Eam Khwang Teoh

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


A new joint parametric and nonparametric curve evolution algorithm is proposed for medical image segmentation. In this algorithm, both the nonlinear space of level set function (nonparametric model) and the linear subspace of level set function spanned by the principle components (parametric model) are employed in the evolution procedure. The nonparametric curve evolution can drive the curve precisely to object boundaries while the parametric model acts as a statistical constraint based on the Bayesian framework in order to match object shape more robustly. As a result, our new algorithm is as robust as the parametric curve evolution algorithms and at the same time, yields more accurate segmentation results by using the shape prior information. Comparative results on segmenting ventricle frontal horns and putamen shapes in MR brain images confirm the advantages of the proposed joint curve evolution algorithm.

Original languageEnglish (US)
Pages (from-to)354-361
Number of pages8
JournalComputerized Medical Imaging and Graphics
Issue number5
StatePublished - Jul 2010


  • Curve evolution
  • Level set method
  • MRI
  • Shape prior

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Health Informatics
  • Radiological and Ultrasound Technology
  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition


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