Computerized Classification of Lacunar Infarcts and Enlarged Virchow-Robin Spaces in Brain MR Images

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  • KUNIEDA Takuya
    Department of Intelligent Image Information, Graduate School of Medicine, Gifu University
  • UCHIYAMA Yoshikazu
    Department of Biomedical Informatics, Graduate School of Medicine, Gifu University
  • HARA Takeshi
    Department of Intelligent Image Information, Graduate School of Medicine, Gifu University
  • FUJITA Hiroshi
    Department of Intelligent Image Information, Graduate School of Medicine, Gifu University
  • KATO Hiroki
    Department of Radiology, Graduate School of Medicine, Gifu University
  • ASANO Takahiko
    Department of Radiology, Graduate School of Medicine, Gifu University
  • KANEMATSU Masayuki
    Department of Radiology, Graduate School of Medicine, Gifu University
  • HOSHI Hiroaki
    Department of Radiology, Graduate School of Medicine, Gifu University
  • IWAMA Toru
    Department of Neurosurgery, Graduate School of Medicine, Gifu University
  • KINOSADA Yasutomi
    Department of Biomedical Informatics, Graduate School of Medicine, Gifu University
  • YOKOYAMA Kazutoshi
    Department of Neurosurgery, Kizawa Memorial Hospital
  • SHINODA Jun
    Department of Neurosurgery, Kizawa Memorial Hospital

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Other Title
  • 脳MR画像におけるラクナ梗塞と血管周囲腔拡大の鑑別法

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The detection of asymptomatic lacunar infarcts on magnetic resonance (MR) images is important because their presence indicates an increased risk of severe cerebral infarction. However, accurate identification of lacunar infarcts on MR images is often hard for radiologists because of the difficulty in distinguishing lacunar infarcts and enlarged Virchow-Robin spaces. Therefore, we developed a computer-aided diagnosis (CAD) scheme for the classification of lacunar infarcts and enlarged Virchow-Robin spaces. Our database consisted of T1-and T2-weighted images obtained from 52 patients, which included 89 lacunar infarcts and 20 enlarged Virchow-Robin spaces. The locations of lacunar infarcts and enlarged Virchow-Robin spaces were determined by experienced neuroradiologists. We first enhanced the lesions in T2-weighted image by using the white top-hat transformation. A gray-level thresholding was then applied to the enhanced image for the segmentation of lesions. From the segmented lesions, we determined image features, such as size, shape, location, and signal intensities in T1-and T2-weighted images. A neural network was then employed for distinguishing between lacunar infarcts and enlarged Virchow-Robin spaces. Our computerized method was evaluated by using a leave-one-out method. The result indicated that the area under the ROC curve was 0.893. Therefore, our CAD scheme would be useful in assisting radiologists for distinguishing between lacunar infarcts and enlarged Virchow-Robin spaces in MR images.

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