Preliminary Study on Automated Detection of Cerebral Vessels from Head CTA Images

  • INOMATA SATOMI
    Department of Radiological Technology, School of Health Sciences, Faculty of Medicine, Niigata University : (Present Address)Niigata University Medical & Dental Hospital
  • LEE YONGBUM
    Department of Radiological Technology, School of Health Sciences, Faculty of Medicine, Niigata University
  • TSAI DU-YIH
    Department of Radiological Technology, School of Health Sciences, Faculty of Medicine, Niigata University
  • YOKOYAMA RYUJIRO
    Division of Regeneration and Advanced Medical Sciences, Graduate School of Medicine, Gifu University
  • HARA TAKESHI
    Division of Regeneration and Advanced Medical Sciences, Graduate School of Medicine, Gifu University
  • FUJITA HIROSHI
    Division of Regeneration and Advanced Medical Sciences, Graduate School of Medicine, Gifu University
  • KANEMATSU MASAYUKI
    Department of Radiology, Gifu University School of Medicine
  • IWAMA TORU
    Department of Neurosurgery, Gifu University School of Medicine
  • HOSHI HIROAKI
    Department of Radiology, Gifu University School of Medicine

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Other Title
  • 頭部CTA画像における脳血管領域の自動抽出の試み

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We propose an approach for automated detection of cerebral vessels from head CT angiographic images. This approach contains two major features. First, instead of using the well-known image-processing techniques such as thresholding and labeling, a novel Laplacian-like filter is developed and employed in the region of interest in an image to be processed. Second, not only is the axial-view image reconstructed from head CT angiographic images used, but, in addition, the sagittal-and coronal-view images are reconstructed and used. By applying these major features in the process of detection of brain vessels, more accurate results can be achieved. To validate the effectiveness of the proposed method, we applied the method to three clinical cases, all of which were head CT angiograms. Our preliminary results showed that the proposed method has the potential to automatically detect cerebral vessels in head CT angiograms with acceptable accuracy.

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