Set-based Cascading Approaches for Magnetic Resonance (MR) Image Segmentation (SCAMIS).
Conference Proceeding Article
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.
Intelligent Systems and Decision Analytics
American Medical Informatics Association Annual Symposium (AMIA)
City or Country
Washington DC, USA
Liu, J; Tze-Yun LEONG; Chee KB; Tan BP; Shuter B; and Wang SC.
Set-based Cascading Approaches for Magnetic Resonance (MR) Image Segmentation (SCAMIS).. (2006). American Medical Informatics Association Annual Symposium (AMIA). 504-508. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3040