局所濃度値特徴と多クラス識別器を用いた腹部3次元CT像からの肝血管腫検出  [in Japanese] Detection of the liver hemangioma from 3D abdominal CT images by using local intensity features and multi-class classifier  [in Japanese]

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Abstract

本稿では,4時相の腹部3次元CT像からの肝血管腫検出手法を提案する.本手法では,まず門脈相CT像から肝血管腫の候補領域を検出する.事前に生成した濃度値分布クラスを用いて各画素の事後確率を計算し,各画素の事後確率が最も高くなる濃度値分布クラスに画素を分類することで,肝血管腫候補領域を得る.得られた肝血管腫候補領域から過検出の原因となる肝臓血管を抽出し,肝血管腫候補領域から削除する.次に,肝血管腫候補領域に対して塊状構造強調フィルタやガウシアンフィルタ,メディアンフィルタなどの特徴量計測用フィルタを適用し,フィルタの出力値から各画素ごとの特徴量ベクトルを生成する.各画素の特徴量ベクトルから多クラス識別器により各画素のカテゴリを決定し,肝血管腫であるとカテゴリ分けされた領域を最終的な肝血管腫領域として検出する.腹部3次元CT像15症例に対して実験を行ったところ,検出結果の領域単位における再現率は83.3%,拾い過ぎ数は15.4個/画像であった. In this paper, we propose a method for detecting hemangioma from four-phases contrasted 3D abdominal CT images. In our method, candidate regions of the liver hemangioma are detected from a portal phase CT image. The candidate regions of the liver hemangioma are obtained by classifying voxels based on the posterior probability of each voxel in a liver region. Since false positives of detection results usually include blood vessel regions, we remove the blood vessel regions in the liver from the candidate regions. Then, we calculate a feature value vector at each voxel in the candidate regions. Feature value vectors are calculated by using various filters such as median, gaussian, and blob enhancement filters. A category of each feature value vector is determined by a multi-class classifier. Finally, we obtain regions whose category is classified as the liver hemangioma. We apply this method to 15 cases of abdominal CT images. Recall rates of the segmentation accuracy and the number of False Positives were 83.3% and 15.4 per case, respectively.

In this paper, we propose a method for detecting hemangioma from four-phases contrasted 3D abdominal CT images. In our method, candidate regions of the liver hemangioma are detected from a portal phase CT image. The candidate regions of the liver hemangioma are obtained by classifying voxels based on the posterior probability of each voxel in a liver region. Since false positives of detection results usually include blood vessel regions, we remove the blood vessel regions in the liver from the candidate regions. Then, we calculate a feature value vector at each voxel in the candidate regions. Feature value vectors are calculated by using various filters such as median, gaussian, and blob enhancement filters. A category of each feature value vector is determined by a multi-class classifier. Finally, we obtain regions whose category is classified as the liver hemangioma. We apply this method to 15 cases of abdominal CT images. Recall rates of the segmentation accuracy and the number of False Positives were 83.3% and 15.4 per case, respectively.

Journal

  • IEICE technical report.

    IEICE technical report. 111(389), 383-388, 2012-01-12

    The Institute of Electronics, Information and Communication Engineers

References:  13

Codes

  • NII Article ID (NAID)
    110009481672
  • NII NACSIS-CAT ID (NCID)
    AA11370335
  • Text Lang
    JPN
  • Article Type
    ART
  • ISSN
    0913-5685
  • NDL Article ID
    023424649
  • NDL Call No.
    Z16-940
  • Data Source
    CJP  NDL  NII-ELS  IR 
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