Applying training hidden features to joint curve evolution for brain MRI segmentation

Mahshid Farzinfar, Eam Khwang Teoh, Zhong Xue

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

Abstract

According to the level of information provided in images, segmentation techniques can be categorized into two groups. One is region-labeling, which obeys the intensity-based classification methods. Although modeling the tissue intensity is straightforward by applying local statistical methods and spatial dependencies, the results might suffer from noise and incomplete data. The second group of techniques applies active contour models, in which the objective is to find the optimal partition of the image domain using a closed or open curve by using prior constraints on the shape variation. However, estimating optimal curve is intractable due to the incomplete observation data. This paper extends a previously reported joint active contour model for medical image segmentation in a new Expectation-Maximization (EM) framework, wherein the evolution curve is constrained not only by a shape-based statistical model but also by applying a hidden variable model from the image observation. In this approach, the hidden variable model is defined by the local voxel labeling computed from its likelihood function, depended on the image functions and the prior anatomical knowledge. Comparative results on segmenting putamen and caudate shapes in MR brain images confirmed both robustness and accuracy of the proposed curve evolution algorithm.

Original languageEnglish (US)
Title of host publication11th International Conference on Control, Automation, Robotics and Vision, ICARCV 2010
Pages1187-1192
Number of pages6
DOIs
StatePublished - 2010
Event11th International Conference on Control, Automation, Robotics and Vision, ICARCV 2010 - Singapore, Singapore
Duration: Dec 7 2010Dec 10 2010

Publication series

Name11th International Conference on Control, Automation, Robotics and Vision, ICARCV 2010

Other

Other11th International Conference on Control, Automation, Robotics and Vision, ICARCV 2010
CountrySingapore
CitySingapore
Period12/7/1012/10/10

Keywords

  • Active contours
  • Expectation-Maximization algorithm
  • Level set methods
  • Statistical shape model

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Systems Engineering

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