Abstract:
:In a computer-aided diagnosis (CAD) system, especially for chest radiograph or chest X-ray (CXR) screening, CXR image view information is required. Automatically separating CXR image view, frontal and lateral can ease subsequent CXR screening process, since the techniques may not equally work for both views. We present a novel technique to classify frontal and lateral CXR images, where we introduce angular relational signature through force histogram to extract features and apply three different state-of-the-art classifiers: multi-layer perceptron, random forest, and support vector machine to make a decision. We validated our fully automatic technique on a set of 8100 images hosted by the U.S. National Library of Medicine (NLM), National Institutes of Health (NIH), and achieved an accuracy close to 100%. Our method outperforms the state-of-the-art methods in terms of processing time (less than or close to 2 s for the whole test data) while the accuracies can be compared, and therefore, it justifies its practicality. Graphical Abstract Interpreting chest X-ray (CXR) through the angular relational signature.
journal_name
Med Biol Eng Computjournal_title
Medical & biological engineering & computingauthors
Santosh KC,Wendling Ldoi
10.1007/s11517-018-1786-3subject
Has Abstractpub_date
2018-08-01 00:00:00pages
1447-1458issue
8eissn
0140-0118issn
1741-0444pii
10.1007/s11517-018-1786-3journal_volume
56pub_type
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