title: [Improvements to an automated method for detecting carotid artery calcifications by adopting a positional feature and feature selection].
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.
authors: Department of Intelligent Image Information, Division of Regeneration and Advanced Medical Sciences, Graduate School of Medicine.
authors: Ryo
authors: R
authors: Takahashi
authors: Chisako
authors: C
authors: Muramatsu
authors: Takeshi
authors: T
authors: Hara
authors: Tatsuro
authors: T
authors: Hayashi
authors: Akitoshi
authors: A
authors: Katsumata
authors: Xiangrong
authors: X
authors: Zhou
authors: Hiroshi
authors: H
authors: Fujita
mesh_terms: D002339:Carotid Arteries / Q000000981:diagnostic imaging*; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D011862:Radiography, Panoramic / Q000379:methods; D061205:Vascular Calcification / Q000000981:diagnostic imaging*