Combination of Radiological and Gray Level Co-occurrence Matrix Textural Features Used to Distinguish Solitary Pulmonary Nodules by Computed Tomography.
The objective of this study was to investigate the method of the combination of radiological and textural features for the differentiation of malignant from benign solitary pulmonary nodules by computed tomography. Features including 13 gray level co-occurrence matrix textural features and 12 radiol...
| Publicado en: | Journal of Digital Imaging Vol. 26; no. 4; pp. 797 - 803 |
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| Autores principales: | , , , , , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2013
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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=104190863&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104190863 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2013 vid: 26 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104190863 88934658 10.1007/s10278-012-9547-6 NLM23325122 PMC3705005 104190863 ppf: 797 ppct: 6 formats: fmt: @attributes: type: P tig: atl: Combination of Radiological and Gray Level Co-occurrence Matrix Textural Features Used to Distinguish Solitary Pulmonary Nodules by Computed Tomography. aug: au: Wu, Haifeng Sun, Tao Wang, Jingjing Li, Xia Wang, Wei Huo, Da Lv, Pingxin He, Wen Wang, Keyang Guo, Xiuhua affil: School of Public Health and Family Medicine, Capital Medical University, Beijing 100069 China sug: subj: Neural Networks (Computer) Lung Neoplasms Diagnosis Lung Diseases Diagnosis Diagnosis, Differential Tomography, X-Ray Computed Radiographic Image Interpretation, Computer-Assisted Radiographic Image Enhancement Evaluation Research ROC Curve Logistic Regression Female Male Human Funding Source Female Male ab: The objective of this study was to investigate the method of the combination of radiological and textural features for the differentiation of malignant from benign solitary pulmonary nodules by computed tomography. Features including 13 gray level co-occurrence matrix textural features and 12 radiological features were extracted from 2,117 CT slices, which came from 202 (116 malignant and 86 benign) patients. Lasso-type regularization to a nonlinear regression model was applied to select predictive features and a BP artificial neural network was used to build the diagnostic model. Eight radiological and two textural features were obtained after the Lasso-type regularization procedure. Twelve radiological features alone could reach an area under the ROC curve (AUC) of 0.84 in differentiating between malignant and benign lesions. The 10 selected characters improved the AUC to 0.91. The evaluation results showed that the method of selecting radiological and textural features appears to yield more effective in the distinction of malignant from benign solitary pulmonary nodules by computed tomography. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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