An Assisted Diagnosis System for Detection of Early Pulmonary Nodule in Computed Tomography Images.
Lung cancer is still the most concerned disease around the world. Lung nodule generates in the pulmonary parenchyma which indicates the latent risk of lung cancer. Computer-aided pulmonary nodules detection system is necessary, which can reduce diagnosis time and decrease mortality of patients. In t...
| Publicado en: | Journal of Medical Systems Vol. 41; no. 2; pp. 1 - 10 |
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| Autores principales: | , , , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2017
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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=120895346&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 120895346 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Feb2017 vid: 41 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 120895346 120895346 120895346 10.1007/s10916-016-0669-0 120895346 ppf: 1 ppct: 9 formats: fmt: @attributes: type: P tig: atl: An Assisted Diagnosis System for Detection of Early Pulmonary Nodule in Computed Tomography Images. aug: au: Liu, Ji-kui Jiang, Hong-yang Gao, Meng-di He, Chen-guang Wang, Yu Wang, Pu Ma, He li, Ye affil: Key Laboratory for Health Informatics of the Chinese Academy of Sciences (HICAS) , Shenzhen Institutes of Advanced Technology , Shenzhen 518055 China sug: subj: Lung Neoplasms Diagnosis Early Diagnosis Tomography, X-Ray Computed Radiographic Image Interpretation, Computer-Assisted Methods Human China Funding Source Diagnosis, Computer Assisted Sensitivity and Specificity Databases Validity Descriptive Statistics ROC Curve ab: Lung cancer is still the most concerned disease around the world. Lung nodule generates in the pulmonary parenchyma which indicates the latent risk of lung cancer. Computer-aided pulmonary nodules detection system is necessary, which can reduce diagnosis time and decrease mortality of patients. In this study, we have proposed a new computer aided diagnosis (CAD) system for detection of early pulmonary nodule, which can help radiologists quickly locate suspected nodules and make judgments. This system consists of four main sections: pulmonary parenchyma segmentation, nodule candidate detection, features extraction (total 22 features) and nodule classification. The publicly available data set created by the Lung Image Database Consortium (LIDC) is used for training and testing. This study selects 6400 slices from 80 CT scans containing totally 978 nodules, which is labeled by four radiologists. Through a fast segmentation method proposed in this paper, pulmonary nodules including 888 true nodules and 11,379 false positive nodules are segmented. By means of an ensemble classifier, Random Forest (RF), this study acquires 93.2, 92.4, 94.8, 97.6% of accuracy, sensitivity, specificity, area under the curve (AUC), respectively. Compared with support vector machine (SVM) classifier, RF can reduce more false positive nodules and acquire larger AUC. With the help of this CAD system, radiologist can be provided with a great reference for pulmonary nodule diagnosis timely. 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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