Cerebral white matter segmentation from MRI using probabilistic graph cuts and geometric shape priors

Ananda S. Chowdhury, Ashish K. Rudra, Mainak Sen, Ahmed Elnakib, Ayman El-Baz

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

20 Scopus citations


Study of cerebral white matter in the brain is an important medical problem which helps in better understanding of brain disorders like autism. The goal of this research is to segment the cerebral white matter from the input Magnetic Resonance Imaging (MRI) data. The present segmentation problem becomes extremely difficult due to i) the complex shape of the cerebral white matter and ii) the very low contrast between the white matter and the surrounding structures in the MRI data. We employ a novel probabilistic graph cut algorithm, where the edge capacity functions of the classical graph cut algorithm are modified according to the probabilities of pixels to belong to different segmentation classes. In order to separate the surrounding structures from the white matter, two appropriate geometric shape priors are introduced. Experimentation in 2D with 20 different datasets has yielded an average segmentation accuracy of 94.78%.

Original languageEnglish (US)
Title of host publication2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings
Number of pages4
StatePublished - 2010
Event2010 17th IEEE International Conference on Image Processing, ICIP 2010 - Hong Kong, Hong Kong
Duration: Sep 26 2010Sep 29 2010

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880


Other2010 17th IEEE International Conference on Image Processing, ICIP 2010
Country/TerritoryHong Kong
CityHong Kong

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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