Support vector machine (SVM) active learning for automated Glioblastoma segmentation

Po Su, Zhong Xue, Linda Chi, Jianhua Yang, Stephen T. Wong

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

8 Scopus citations

Abstract

Accurate segmentation of Glioblastoma multiforme (GBM) from MR images is important for sub-typing in diagnosis, determining tumor margins in surgical planning, and selecting appropriate therapies. However, it is a challenging and time-consuming task because GBM has a variety of imaging characteristics and often deforms nearby tissues in the brain. In this paper, we propose a support vector machine (SVM) active learning approach to address the problem of GBM segmentation from multi-modal MR images. First, a knowledge-based fuzzy clustering algorithm is performed to segment the brain tissues into six classes including white matter (WM), grey matter (GM), cerebrospinal fluid (CSF), T2-hyperintense regions, necrosis and enhanced tumor. Then, the SVM active learning approach is applied to refine the segmentation. Comparative studies with other segmentation methods indicate that the proposed algorithm can segment GBM more accurately.

Original languageEnglish (US)
Title of host publication2012 9th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, ISBI 2012 - Proceedings
Pages598-601
Number of pages4
DOIs
StatePublished - Aug 15 2012
Event2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012 - Barcelona, Spain
Duration: May 2 2012May 5 2012

Other

Other2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012
CountrySpain
CityBarcelona
Period5/2/125/5/12

Keywords

  • active learning
  • clustering
  • Glioblastoma
  • SVM

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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