Improvements to an Automated Method for Detecting Carotid Artery Calcifications by Adopting a Positional Feature and Feature Selection

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  • Takahashi Ryo
    Department of Intelligent Image Information, Division of Regeneration and Advanced Medical Sciences, Graduate School of Medicine
  • Muramatsu Chisako
    Department of Intelligent Image Information, Division of Regeneration and Advanced Medical Sciences, Graduate School of Medicine
  • Hara Takeshi
    Department of Intelligent Image Information, Division of Regeneration and Advanced Medical Sciences, Graduate School of Medicine
  • Hayashi Tatsuro
    Media Co., Ltd.
  • Katsumata Akitoshi
    Department of Oral Radiology, Asahi University School of Dentistry
  • Zhou Xiangrong
    Department of Intelligent Image Information, Division of Regeneration and Advanced Medical Sciences, Graduate School of Medicine
  • Fujita Hiroshi
    Department of Intelligent Image Information, Division of Regeneration and Advanced Medical Sciences, Graduate School of Medicine

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Other Title
  • 位置特徴量の導入と特徴量選択による頸動脈石灰化の自動検出法の改良
  • イチ トクチョウリョウ ノ ドウニュウ ト トクチョウリョウ センタク ニ ヨル ケイドウミャク セッカイカ ノ ジドウ ケンシュツホウ ノ カイリョウ

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Abstract

The purpose of this study was to improve an automated scheme for detecting carotid artery calcification (CAC) in dental panoramic radiographs (DPRs). Using 100 DPRs, the sensitivity of CAC detection employing our previous method was 90.0% with 5.0 false positives (FPs) per image. This study describes two enhancements. One is the adoption of a new feature for the position of CACs in addition to previous features. The other is feature selection employing the support vector machine using all combinations. Five of 12 features were selected. Using our proposed method, the average sensitivity for the same database proved to be 90.0%, with only 2.5 FPs per image. These results indicate the potential effectiveness of the new positional feature and feature selection.

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