Journal of Biomedical Engineering Research (대한의용생체공학회:의공학회지)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
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Fully Automatic Segmentation Method of Pathological Periventricular White Matter Changes Using Morphological Features

  • Cho Ik-Hwan (Department of Electronic Engineering, College of Engineering, Inha University) ;
  • Song In-Chan (Department of Radiology, Seoul National University Hospital) ;
  • Oh Jung-Su (Interdisciplinary Program of Biomedical Engineering, Seoul National University) ;
  • Jeong Dong-Seok (Department of Electronic Engineering, College of Engineering, Inha University)
  • Published : 2005.12.01
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Abstract

Age-related White Matter Changes (WMC) on Magnetic Resonance Imaging (MRI) are known to appear frequently in Multiple sclerosis (MS) and Alzheimer's disease and to be related to cognitive impairment. The characterization of these WMC is very important to the study of psychology and aging. These changes consist of periventricular and subcortical types, however it is difficult to detect and segment WMC using only intensity-based methods, because their intensity, level IS similar to th~t of the gray matter (GM). In this paper, we propose a new method of segmenting periventricular WMC using K-means clustering and morphological features.

Keywords

References

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