PICE: Prior information constrained evolution for 3-D and 4-D brain tumor segmentation

Xiaojun Xue, Zhong Xue, Fei Cao, Ying Zhu, Geoffrey S. Young, Yan Li, Jianhua Yang, Stephen T.C. Wong

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

5 Scopus citations

Abstract

Brain tumor segmentation is an important image processing step in diagnosis, treatment planning, and follow-up studies of Glioblastoma (GBM). However it is still a challenging task due to varying in size, shape, location, and image intensities within and around the tumor. In this paper, we propose a new brain tumor segmentation method for T1-weighted MR brain images based on an improved level set method using prior information as a constraint, called Prior Information Constrained Evolution (PICE). A new energy function in PICE incorporating the tumor intensity prior is designed to match brain tumor more accurately. The advantage of PICE has been illustrated by comparing with the traditional level set method in 3-D. In addition, we also illustrate that PICE can be easily applied to 4-D images, which facilitates follow-up studies of brain tumor treatments. Using longitudinal GBM data from five patients we showed the advantages of the proposed algorithm.

Original languageEnglish (US)
Title of host publication2010 7th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, ISBI 2010 - Proceedings
Pages840-843
Number of pages4
DOIs
StatePublished - 2010
Event7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010 - Rotterdam, Netherlands
Duration: Apr 14 2010Apr 17 2010

Publication series

Name2010 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010 - Proceedings

Other

Other7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010
CountryNetherlands
CityRotterdam
Period4/14/104/17/10

Keywords

  • Level set
  • Magnetic resonance imaging
  • Prior distribution
  • Tumor segmentation

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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