Contourlet textual features: improving the diagnosis of solitary pulmonary nodules in two dimensional CT images

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dc.contributor.author Wang, Jingjing
dc.contributor.author Sun, Tao
dc.contributor.author Gao, Ni
dc.contributor.author Menon, Desmond Dev
dc.contributor.author Luo, Yanxia
dc.contributor.author Gao, Qi
dc.contributor.author Li, Xia
dc.contributor.author Wang, Wei
dc.contributor.author Zhu, Huiping
dc.contributor.author Lv, Pingxin
dc.contributor.author Liang, Zhigang
dc.contributor.author Tao, Lixin
dc.contributor.author Liu, Xiangtong
dc.contributor.author Guo, Xiuhua
dc.date.accessioned 2016-02-17T11:43:38Z
dc.date.available 2016-02-17T11:43:38Z
dc.date.issued 2014
dc.identifier.citation Wang J, Sun T, Gao N, Menon DD, Luo Y, Gao Q, et al. (2014) Contourlet Textual Features: Improving the Diagnosis of Solitary Pulmonary Nodules in Two Dimensional CT Images. PLoS ONE 9(9): e108465. doi:10.1371/journal.pone.0108465
dc.identifier.volume 9 en
dc.identifier.issued 9 en
dc.identifier.issn 1932-6203
dc.identifier.uri http://hdl.handle.net/10468/2325
dc.identifier.doi 10.1371/journal.pone.0108465
dc.description.abstract Objective: To determine the value of contourlet textural features obtained from solitary pulmonary nodules in two dimensional CT images used in diagnoses of lung cancer. Materials and Methods: A total of 6,299 CT images were acquired from 336 patients, with 1,454 benign pulmonary nodule images from 84 patients (50 male, 34 female) and 4,845 malignant from 252 patients (150 male, 102 female). Further to this, nineteen patient information categories, which included seven demographic parameters and twelve morphological features, were also collected. A contourlet was used to extract fourteen types of textural features. These were then used to establish three support vector machine models. One comprised a database constructed of nineteen collected patient information categories, another included contourlet textural features and the third one contained both sets of information. Ten-fold cross-validation was used to evaluate the diagnosis results for the three databases, with sensitivity, specificity, accuracy, the area under the curve (AUC), precision, Youden index, and F-measure were used as the assessment criteria. In addition, the synthetic minority over-sampling technique (SMOTE) was used to preprocess the unbalanced data. Results: Using a database containing textural features and patient information, sensitivity, specificity, accuracy, AUC, precision, Youden index, and F-measure were: 0.95, 0.71, 0.89, 0.89, 0.92, 0.66, and 0.93 respectively. These results were higher than results derived using the database without textural features (0.82, 0.47, 0.74, 0.67, 0.84, 0.29, and 0.83 respectively) as well as the database comprising only textural features (0.81, 0.64, 0.67, 0.72, 0.88, 0.44, and 0.85 respectively). Using the SMOTE as a pre-processing procedure, new balanced database generated, including observations of 5,816 benign ROIs and 5,815 malignant ROIs, and accuracy was 0.93. Conclusion: Our results indicate that the combined contourlet textural features of solitary pulmonary nodules in CT images with patient profile information could potentially improve the diagnosis of lung cancer. en
dc.description.sponsorship National Natural Science Foundation of China (81172772); Beijing Municipal Natural Science Foundation, China (4112015, 7131002) en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Public Library of Science en
dc.rights © 2015 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited en
dc.rights.uri http://creativecommons.org/licenses/by/4.0/ en
dc.subject Support vector machine en
dc.subject Computer-aided diagnosis en
dc.subject Lung cancer en
dc.subject Tomography en
dc.subject Curvelet en
dc.subject Probability en
dc.subject Performance en
dc.subject Transform en
dc.subject Wavelet en
dc.subject Scheme en
dc.title Contourlet textual features: improving the diagnosis of solitary pulmonary nodules in two dimensional CT images en
dc.type Article (peer-reviewed) en
dc.internal.authorcontactother Xia Li, Department of Epidemiology and Public Health, University College Cork, Cork, Ireland. +353-21-490-3000 Email: xia.lee@ucc.ie en
dc.internal.availability Full text available en
dc.description.version Published Version en
dc.internal.wokid WOS:000342492700131
dc.contributor.funder National Natural Science Foundation of China
dc.contributor.funder Beijing Municipal Natural Science Foundation
dc.description.status Peer reviewed en
dc.identifier.journaltitle PLOS ONE en
dc.internal.IRISemailaddress xia.lee@ucc.ie en
dc.identifier.articleid e108465


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© 2015 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Except where otherwise noted, this item's license is described as © 2015 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
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