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

• Main Authors: Liu, Jiang, Tze-Yun LEONG, Chee, Kin Ban, Tan, Boon Pin, Shuter, Borys, Wang, Shih Chang
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2006
• Subjects: Computer Sciences; Health Information Technology
• Online Access: https://ink.library.smu.edu.sg/sis_research/3040; https://ink.library.smu.edu.sg/context/sis_research/article/4040/viewcontent/Set_based_Cascading_Approaches_for_Magnetic_Reson.pdf
• Institution: Singapore Management University

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.