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Proceedings Paper

Fully automated calculation of cardiothoracic ratio in digital chest radiographs
Author(s): Lin Cong; Luan Jiang; Gang Chen; Qiang Li
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Paper Abstract

The calculation of Cardiothoracic Ratio (CTR) in digital chest radiographs would be useful for cardiac anomaly assessment and heart enlargement related disease indication. The purpose of this study was to develop and evaluate a fully automated scheme for calculation of CTR in digital chest radiographs. Our automated method consisted of three steps, i.e., lung region localization, lung segmentation, and CTR calculation. We manually annotated the lung boundary with 84 points in 100 digital chest radiographs, and calculated an average lung model for the subsequent work. Firstly, in order to localize the lung region, generalized Hough transform was employed to identify the upper, lower, and outer boundaries of lung by use of Sobel gradient information. The average lung model was aligned to the localized lung region to obtain the initial lung outline. Secondly, we separately applied dynamic programming method to detect the upper, lower, outer and inner boundaries of lungs, and then linked the four boundaries to segment the lungs. Based on the identified outer boundaries of left lung and right lung, we corrected the center and the declination of the original radiography. Finally, CTR was calculated as a ratio of the transverse diameter of the heart to the internal diameter of the chest, based on the segmented lungs. The preliminary results on 106 digital chest radiographs showed that the proposed method could obtain accurate segmentation of lung based on subjective observation, and achieved sensitivity of 88.9% (40 of 45 abnormalities), and specificity of 100% (i.e. 61 of 61 normal) for the identification of heart enlargements.

Paper Details

Date Published: 3 March 2017
PDF: 6 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013432 (3 March 2017); doi: 10.1117/12.2254398
Show Author Affiliations
Lin Cong, Shanghai United Imaging Healthcare Co., Ltd. (China)
Luan Jiang, Shanghai United Imaging Healthcare Co., Ltd. (China)
Shanghai Advanced Research Institute (China)
Gang Chen, Shanghai United Imaging Healthcare Co., Ltd. (China)
Qiang Li, Shanghai United Imaging Healthcare Co., Ltd. (China)
Shanghai Advanced Research Institute (China)


Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato; Nicholas A. Petrick, Editor(s)

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