3D shape analysis to reduce false positives for lung nodule detection systems.
Using images from the Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), we developed a methodology for classifying lung nodules. The proposed methodology uses image processing and pattern recognition techniques. To classify volumes of interest into nodules and non-nodules...
| Published in: | Medical & Biological Engineering & Computing Vol. 55; no. 8; pp. 1199 - 1214 |
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| Main Authors: | , , , , , , , , |
| Format: | Journal Article |
| Published: |
Springer Nature
Aug2017
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=124485693&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 124485693 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Aug2017 vid: 55 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 124485693 124485693 144039061 NLM27752930 10.1007/s11517-016-1582-x NLM27752930 124485693 ppf: 1199 ppct: 15 formats: fmt: @attributes: type: P tig: atl: 3D shape analysis to reduce false positives for lung nodule detection systems. aug: au: Filho, Antonio Silva, Aristófanes Paiva, Anselmo Nunes, Rodolfo Gattass, Marcelo Filho, Antonio Oseas de Carvalho Silva, Aristófanes Corrêa de Paiva, Anselmo Cardoso Nunes, Rodolfo Acatauassú affil: Federal University of Maranhão - UFMA, Applied Computing Group - NCA , Av. dos Portugueses, SN, Campus do Bacanga, Bacanga São Luís 65085-580 Brazil sug: subj: Algorithms Tomography, X-Ray Computed Methods Imaging, Three-Dimensional Methods Solitary Pulmonary Nodule Radiographic Image Interpretation, Computer-Assisted Methods Solitary Pulmonary Nodule Pathology Information Science Methods Reproducibility of Results False Positive Results Middle Age Female Male Radiographic Image Enhancement Methods Sensitivity and Specificity Middle Aged: 45-64 years Female Male ab: Using images from the Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), we developed a methodology for classifying lung nodules. The proposed methodology uses image processing and pattern recognition techniques. To classify volumes of interest into nodules and non-nodules, we used shape measurements only, analyzing their shape using shape diagrams, proportion measurements, and a cylinder-based analysis. In addition, we use the support vector machine classifier. To test the proposed methodology, it was applied to 833 images from the LIDC-IDRI database, and cross-validation with k-fold, where [Formula: see text], was used to validate the results. The proposed methodology for the classification of nodules and non-nodules achieved a mean accuracy of 95.33 %. Lung cancer causes more deaths than any other cancer worldwide. Therefore, precocious detection allows for faster therapeutic intervention and a more favorable prognosis for the patient. Our proposed methodology contributes to the classification of lung nodules and should help in the diagnosis of lung cancer. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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