A Hybrid Approach using Set Theory (HAST) for Magnetic Resonance (MR) Image segmentation

Liu Jiang, Chee Kin Ban, Tan Boon Pin, Shuter Borys, Shih Chang Wang

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

Abstract

This paper describes a new Hybrid Approach using Set Theory (HAST) for Magnetic Resonance (MR) Image segmentation based on two existing techniques, region-based and level set methods. In our approach, instead of using the typical pipeline methodology to integrate the two techniques, a hybrid set-based methodology will be proposed. To evaluate the effectiveness of HAST, MR images taken from a national hospital that reflects the quality of real world medical images are used. A comparison between the two individual techniques and HAST will also be made to demonstrate the effectiveness of the latter.

Original languageEnglish
Title of host publicationProceedings of the 6th International Workshop on Pattern Recognition in Information Systems, PRIS 2006, in Conjunction with ICEIS 2006
PublisherINSTICC Press
Pages159-168
Number of pages10
ISBN (Print)9728865554, 9789728865559
Publication statusPublished - 2006
Externally publishedYes
Event6th International Workshop on Pattern Recognition in Information Systems, PRIS 2006, in Conjunction with ICEIS 2006 - Paphos, Cyprus
Duration: 23 May 200627 May 2006

Publication series

NameProceedings of the 6th International Workshop on Pattern Recognition in Information Systems, PRIS 2006, in Conjunction with ICEIS 2006

Conference

Conference6th International Workshop on Pattern Recognition in Information Systems, PRIS 2006, in Conjunction with ICEIS 2006
Country/TerritoryCyprus
CityPaphos
Period23/05/0627/05/06

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Information Systems

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