Angular relational signature-based chest radiograph image view classification
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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.
KeywordsAutomation Computer-aided diagnosis system Medical imaging Chest radiograph Angular relational signature Force histogram Frontal view Lateral view Classification Multi-layer perception Random forest Support vector machine
Authors would like to give thanks to Dr. Sameer Antani, staff scientist, Lister Hill National Center for Biomedical Communications (LHNCBC) for his advice and the dataset owned by the U.S. National Library of Medicine, National Institutes of Health (NIH), MD.
Compliance with ethical standards
This article does not contain any studies with human participants or animals performed by any of the authors.
Conflict of interest
Authors declare that they have no conflicts of interest.
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