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Circle-like foreign element detection in chest x-rays using normalized cross-correlation and unsupervised clustering
Author(s): Fatema T. Zohora; Sameer Antani; K. C. Santosh
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Paper Abstract

Presence of foreign objects (buttons, medical devices) adversely impact the performance of the automated chest X-ray (CXR) screening. We present a novel image processing and machine learning technique to detect circle-like foreign elements in CXR images that helps avoid confusions in automated detection of abnormalities, such as nodules and other calcifications. In our technique, we apply normalized cross-correlation using a few templates to collect potential circle-like elements and unsupervised clustering to make a decision. We validated our fully automatic technique on a set of 400 publicly available images hosted by LHNCBC, U.S. National Library of Medicine (NLM), National Institutes of Health (NIH). Our method achieved an accuracy greater than 90% and outperforms existing techniques that are reported in the literature.

Paper Details

Date Published: 2 March 2018
PDF: 6 pages
Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 105741V (2 March 2018); doi: 10.1117/12.2293739
Show Author Affiliations
Fatema T. Zohora, The Univ. of South Dakota (United States)
Sameer Antani, U.S. National Library of Medicine (United States)
K. C. Santosh, The Univ. of South Dakota (United States)

Published in SPIE Proceedings Vol. 10574:
Medical Imaging 2018: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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