Advanced automatic method for detecting maxillary sinusitis on dental panoramic radiographs
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- MIKI Yuma
- Department of Intelligent Image Information, Graduate School of Medicine, Gifu University
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- HARA Takeshi
- Department of Intelligent Image Information, Graduate School of Medicine, Gifu University
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- MURAMATSU Chisako
- Department of Intelligent Image Information, Graduate School of Medicine, Gifu University
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- HAYASHI Tatsuro
- Media Co., Ltd.
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- KATSUMATA Akitoshi
- Department of Oral Radiology, Asahi University School of Dentistry
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- ZHOU Xiangrong
- Department of Intelligent Image Information, Graduate School of Medicine, Gifu University
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- FUJITA Hiroshi
- Department of Intelligent Image Information, Graduate School of Medicine, Gifu University
Bibliographic Information
- Other Title
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- 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
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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
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- Medical Imaging and Information Sciences
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Medical Imaging and Information Sciences 32 (4), 77-80, 2015
MEDICAL IMAGING AND INFORMATION SCIENCES
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Keywords
Details 詳細情報について
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- CRID
- 1390282679630206720
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- NII Article ID
- 130005116556
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- ISSN
- 18804977
- 09101543
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- Text Lang
- ja
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- Data Source
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- JaLC
- CiNii Articles
- KAKEN
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- Abstract License Flag
- Disallowed