A coupled implicit shape-based deformable model for segmentation of MR images

Mahshid Farzinfar, Eam Khwang Teoh, Zhong Xue

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

In this paper, a new coupled implicit shape-based segmentation algorithm is proposed for medical image segmentation. In the method, both region-based and statistical model-based curve evolution algorithms are jointly used to match the object in a new input image. Compared to the previous method that solely uses statistical shape models, our new algorithm is able to match the boundaries of the object shapes more accurately and at the same time, it maintains similar robustness since the same shape prior information is used to regularize the object shapes. Experiments on segmenting the ventricle frontal horn and putamen shapes in MR brain images confirm that the proposed algorithm yields more accurate segmentation results.

Original languageEnglish (US)
Title of host publication2008 10th International Conference on Control, Automation, Robotics and Vision, ICARCV 2008
Pages651-656
Number of pages6
DOIs
StatePublished - 2008
Event2008 10th International Conference on Control, Automation, Robotics and Vision, ICARCV 2008 - Hanoi, Viet Nam
Duration: Dec 17 2008Dec 20 2008

Publication series

Name2008 10th International Conference on Control, Automation, Robotics and Vision, ICARCV 2008

Other

Other2008 10th International Conference on Control, Automation, Robotics and Vision, ICARCV 2008
CountryViet Nam
CityHanoi
Period12/17/0812/20/08

Keywords

  • Deformable model
  • Image segmentation
  • Level set
  • Statistical model

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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