歯科パノラマX線写真における上顎洞炎の検出手法の高度化  [in Japanese] Advanced automatic method for detecting maxillary sinusitis on dental panoramic radiographs  [in Japanese]

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Author(s)

    • 三木 勇磨 MIKI Yuma
    • 岐阜大学大学院医学系研究科知能イメージ情報分野 Department of Intelligent Image Information, 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 Department of Intelligent Image Information, Graduate School of Medicine, Gifu University
    • 村松 千左子 MURAMATSU Chisako
    • 岐阜大学大学院医学系研究科知能イメージ情報分野 Department of Intelligent Image Information, Graduate School of Medicine, Gifu University
    • 周 向栄 ZHOU Xiangrong
    • 岐阜大学大学院医学系研究科知能イメージ情報分野 Department 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 Department of Intelligent Image Information, Graduate School of Medicine, Gifu University

Abstract

In this study, we propose an improved method to detect abnormal regions in maxillary sinuses by accurate determination of regions of interest (ROIs) based on the anatomical information. The ROIs are set based on the automated search of a hard palate and the average widths of tooth crowns. Preferably, dental panoramic radiographs are obtained with the Frankfort plane aligned horizontally. In order to manage the ROI setting in images with inappropriate positioning, the ROI locations are individually adjusted on the basis of the relationship between the reference line and the alveolar line. The proposed method was evaluated with two databases, DB‐1 and DB‐2, consisting of 59 and 39 images. Using the proposed method, the average concordance rate (Jaccard Index) was improved by more than 10%. The areas under the ROC curves for the sinusitis detection were comparable (0.86 and 0.82 for the two databases) with the previous method. As a result, it is expected that the reliability of the computer output was increased with the improved ROI determination.

Journal

  • Medical Imaging and Information Sciences

    Medical Imaging and Information Sciences 32(4), 77-80, 2015

    MEDICAL IMAGING AND INFORMATION SCIENCES

Codes

  • NII Article ID (NAID)
    130005116556
  • Text Lang
    JPN
  • ISSN
    0910-1543
  • Data Source
    J-STAGE 
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