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Fully Automatic Segmentation Method of Pathological Periventricular White Matter Changes Using Morphological Features

Ik-Hwan Cho|In-Chan Song|Jung-Su Oh|Dong-Seok Jeong
대한의용생체공학회 (The Korea Society Of Medical & Biological Engineering)|2005.12|의공학회지 (Journal of biomedical Engineering Research)|제26권 제6호|pp. 383-391

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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 that of the gray matter (GM). In this paper, we propose a new method of segmenting periventricular WMC using K-means clustering and morphological features

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