Computer-aided evaluation method of white matter hyperintensities related to subcortical vascular dementia based on magnetic resonance imaging

Yasuo Kawata, Hidetaka Arimura, Yasuo Yamashita, Taiki Magome, Masafumi Ohki, Fukai Toyofuku, Yoshiharu Higashida, Kazuhiro Tsuchiya

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14 Citations (Scopus)


It has been reported that the severity of subcortical vascular dementia (VaD) correlated with an area ratio of white matter hyperintensity (WMH) regions to the brain parenchyma (WMH area ratio). The purpose of this study was to develop a computer-aided evaluation method of WMH regions for diagnosis of subcortical VaD based on magnetic resonance (MR) images. A brain parenchymal region was segmented based on the histogram analysis of a T1-weigthed image. The WMH regions were segmented on the subtraction image between a T1-weighted and fluid-attenuated inversion-recovery (FLAIR) images using two segmentation methods, i.e., a region-growing technique and a level-set method, which were automatically and adaptively selected on each WMH region based on its image features by using a support vector machine. We applied the proposed method to 33 slices of the three types of MR images with 245 lesions, which were acquired from 10 patients (age range: 64-90 years, mean: 78) with a diagnosis of VaD on a 1.5-T MR imaging scanner. The average similarity index between regions determined by a manual method and the proposed method was 93.5 ± 2.0% for brain parenchymal regions and 78.2 ± 11.0% for WMH regions. The WMH area ratio obtained by the proposed method correlated with that determined by two neuroradiologists with a correlation coefficient of 0.992. The results presented in this study suggest that the proposed method could assist neuroradiologists in the evaluation of WMH regions related to the subcortical VaD.

Original languageEnglish
Pages (from-to)370-376
Number of pages7
JournalComputerized Medical Imaging and Graphics
Issue number5
Publication statusPublished - Jul 2010

All Science Journal Classification (ASJC) codes

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design


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