Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
12-2006
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.
Discipline
Computer Sciences | Health Information Technology
Research Areas
Intelligent Systems and Optimization
Publication
American Medical Informatics Association Annual Symposium 2006 Proceedings
First Page
504
Last Page
508
Publisher
AMIA
City or Country
Washington, DC
Citation
Liu, Jiang; Tze-Yun LEONG; Chee, Kin Ban; Tan, Boon Pin; Shuter, Borys; and Wang, Shih Chang.
Set-based Cascading Approaches for Magnetic Resonance (MR) Image Segmentation (SCAMIS). (2006). American Medical Informatics Association Annual Symposium 2006 Proceedings. 504-508.
Available at: https://ink.library.smu.edu.sg/sis_research/3040
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.