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Foreign Object Detection in Chest X-rays.

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Proc IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2015, International Workshop on Biomedical and Health Informatics, Bethesda, Maryland, Nov. 9-12, 2015, pp: 56-61.
Abstract: 

Automatic analysis of chest X-ray images is one important approach for screening/identifying pulmonary diseases. The existence of foreign objects in the images hinders
the performance of such processing. In this paper, we focus on one type of foreign objects that is often shown in the images of a large dataset of chest X-rays we are working on—the buttons on the gown that the patient is wearing. The method we propose involves four major steps: intensity normalization, low contrast image identification and enhancement, segmentation of lung regions, and button object extraction. Based on the characteristics of the button objects, we applied two methods for the step of button object extraction. One was based on the circular Hough transform; the other was based on the Viola-Jones algorithm. We tested and compared both methods using a ground truth dataset containing 505 button objects. The results demonstrate the effectiveness of the proposed method.

Xue Z, Candemir S, Antani SK, Long LR, Jaeger S, Demner-Fushman D, Thoma GR. Foreign Object Detection in Chest X-rays. Proc IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2015, International Workshop on Biomedical and Health Informatics, Bethesda, Maryland, Nov. 9-12, 2015, pp: 56-61.