Set-based Cascading Approaches for Magnetic Resonance (MR) Image Segmentation (SCAMIS).

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

Research output: Journal PublicationArticlepeer-review

9 Citations (Scopus)

Abstract

This paper introduces Set-based Cascading Approach for Medical Image Segmentation (SCAMIS), a new methodology for segmentation of medical imaging by integrating a number of algorithms. Existing approaches typically adopt the pipeline methodology. Although these methods provide promising results, the results generated are still susceptible to over-segmentation and leaking. In our methodology, we describe how set operations can be utilized to better overcome these problems. To evaluate the effectiveness of this approach, Magnetic Resonance Images taken from a teaching hospital research programme have been utilised, to reflect the real world quality needed for testing in patient datasets. A comparison between the pipeline and set-based methodology is also presented.

Original languageEnglish
Pages (from-to)504-508
Number of pages5
JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
Publication statusPublished - 2006
Externally publishedYes

ASJC Scopus subject areas

  • Medicine (all)

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