Angular relational signature-based chest radiograph image view classification.
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 bo...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 56; no. 8; pp. 1447 - 1459 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Aug2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=130773123&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 130773123 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Aug2018 vid: 56 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 130773123 130773123 NLM29354890 10.1007/s11517-018-1786-3 NLM29354890 130773123 ppf: 1447 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Angular relational signature-based chest radiograph image view classification. aug: au: Santosh, K. C. Wendling, Laurent affil: Department of Computer Science, The University of South Dakota, 414 E Clark St., 57069, Vermillion, SD, USA sug: subj: Radiography, Thoracic Radiographic Image Interpretation, Computer-Assisted Algorithms Databases Scales ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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