A novel approach to remove foreign objects from chest X-ray images

• URL: http://arxiv.org/abs/2008.06828v1
• Date: Sun, 16 Aug 2020 03:06:28 GMT
• Title: A novel approach to remove foreign objects from chest X-ray images
• Authors: Hieu X. Le, Phuong D. Nguyen, Thang H. Nguyen, Khanh N.Q. Le, Thanh T. Nguyen
• Abstract summary: In this paper, we use multi-method to tackle both removal and inpainting chest radiographs. To conclude, we achieved state-of-the-art accuracy.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We initially proposed a deep learning approach for foreign objects inpainting in smartphone-camera captured chest radiographs utilizing the cheXphoto dataset. Foreign objects which can significantly affect the quality of a computer-aided diagnostic prediction are captured under various settings. In this paper, we used multi-method to tackle both removal and inpainting chest radiographs. Firstly, an object detection model is trained to separate the foreign objects from the given image. Subsequently, the binary mask of each object is extracted utilizing a segmentation model. Each pair of the binary mask and the extracted object are then used for inpainting purposes. Finally, the in-painted regions are now merged back to the original image, resulting in a clean and non-foreign-object-existing output. To conclude, we achieved state-of-the-art accuracy. The experimental results showed a new approach to the possible applications of this method for chest X-ray images detection.

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