Abstract
In automated chest X-ray screening (to detect i.e., Tuberculosis for instance), the presence of foreign objects (buttons, medical devices) hinders it’s performance. In this paper, we present a new technique for detecting circular foreign objects, in particular buttons, within chest X-ray (CXR) images. In our technique, we use a pre-processing step that enhances the CXRs. Using these enhanced images, we find the edge images performing four different edge detection algorithms (Sobel, Canny, Prewitt, and Roberts) and after that, we apply some morphological operations to select candidates (image segmentation) in the chest region. Finally, we apply circular Hough transform (CHT) to detect the circular foreign objects on those images. In all tests, our algorithm performed well under a variety of CXRs. We also compared our proposed technique’s performance with existing techniques in literature (Viola-Jones and CHT). Our technique was able to excel performance in terms of both detection accuracy and computational time.
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Notes
- 1.
Note that, we have tested using 50 CXR images.
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Zohora, F.T., Santosh, K.C. (2017). Circular Foreign Object Detection in Chest X-ray Images. In: Santosh, K., Hangarge, M., Bevilacqua, V., Negi, A. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2016. Communications in Computer and Information Science, vol 709. Springer, Singapore. https://doi.org/10.1007/978-981-10-4859-3_35
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DOI: https://doi.org/10.1007/978-981-10-4859-3_35
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