Set-based Cascading Approaches for Magnetic Resonance (MR) Image Segmentation (SCAMIS).
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.
Liu
Jiang
J
Department of Computer Science, School of Computing, National University of Singapore. liujiang@comp.nus.edu.sg
Leong
Tze Yun
TY
Chee
Kin Ban
KB
Tan
Boon Pin
BP
Shuter
Borys
B
Wang
Shih-Chang
SC
D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007668:Kidney / Q000033:anatomy & histology*; D008279:Magnetic Resonance Imaging