MRA画像における脳動脈領域の抽出法:—大規模データベースを用いた評価—  [in Japanese] Automatic segmentation method of cerebral arteries in MRA images::Performance evaluation using large image database  [in Japanese]

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Author(s)

    • 浅野 龍紀 ASANO Tatsunori
    • 岐阜大学大学院医学系研究科知能イメージ情報分野 Dept. of Intelligent Image Information, Graduate School of Medicine, Gifu University
    • 藤田 広志 FUJITA Hiroshi
    • 岐阜大学大学院医学系研究科知能イメージ情報分野 Dept. of Intelligent Image Information, Graduate School of Medicine, Gifu University
    • 内山 良一 UCHIYAMA Yoshikazu
    • 岐阜大学大学院医学系研究科医療情報学分野 Dept. of Biomedical Informatics, Graduate School of Medicine, Gifu University
    • 浅野 隆彦 ASANO Takahiko
    • 岐阜大学大学院医学系研究科放射線医学分野 Dept. of Radiology, Graduate School of Medicine, Gifu University
    • 加藤 博基 KATO Hiroki
    • 岐阜大学大学院医学系研究科放射線医学分野 Dept. of Radiology, Graduate School of Medicine, Gifu University
    • 原 武史 HARA Takeshi
    • 岐阜大学大学院医学系研究科知能イメージ情報分野 Dept. of Intelligent Image Information, Graduate School of Medicine, Gifu University
    • 周 向栄 Zhou Xiangroug
    • 岐阜大学大学院医学系研究科知能イメージ情報分野 Dept. of Intelligent Image Information, Graduate School of Medicine, Gifu University
    • 岩間 亨 IWAMA Toru
    • 岐阜大学大学院医学系研究科脳神経外科分野 Dept. of Neurosurgery, Graduate School of Medicine, Gifu University
    • 星 博昭 HOSHI Hiroaki
    • 岐阜大学大学院医学系研究科放射線医学分野 Dept. of Radiology, Graduate School of Medicine, Gifu University

Abstract

The detection of cerebrovascular diseases such as unruptured aneurysm and stenosis is a major application of magnetic resonance angiography (MRA) . However, their accurate detection is often difficult for radiologists. Therefore, several computer-aided diagnosis (CAD) schemes have been developed in order to assist radiologists with image interpretation. The purpose of this study is to modify our segmentation method of cerebral arteries and its application to a large image database. For the segmentation of cerebral arteries, we first used a gray level transformation to calibrate voxel values. To adjust for variations in the positioning of patients, image registration was subsequently employed to maximize the overlapping of the cerebral arteries in the target image and reference image. The cerebral arteries were then segmented from the background using gray-level thresholding and region growing techniques. Finally, rule-based schemes with features such as size and anatomical location were employed to distinguish between cerebral arteries and false positives. Our method was applied to 876 clinical cases, which were obtained from three different hospitals. The segmentation of cerebral arteries in 98.1% (859/876) of the MRA studies was attained as an acceptable result. Therefore, our computerized method would be useful for the segmentation of cerebral arteries in MRA images.

Journal

  • Medical Imaging and Information Sciences

    Medical Imaging and Information Sciences 27(3), 55-60, 2010

    MEDICAL IMAGING AND INFORMATION SCIENCES

Cited by:  1

Codes

  • NII Article ID (NAID)
    130000309765
  • Text Lang
    JPN
  • Article Type
    Journal Article
  • ISSN
    0910-1543
  • Data Source
    CJPref  J-STAGE 
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