Shape and Texture Based Novel Features for Automated Juxtapleural Nodule Detection in Lung CTs.
Lung cancer is one of the types of cancer with highest mortality rate in the world. In case of early detection and diagnosis, the survival rate of patients significantly increases. In this study, a novel method and system that provides automatic detection of juxtapleural nodule pattern have been dev...
| Publicado en: | Journal of Medical Systems Vol. 39; no. 5; pp. 1 - 14 |
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| Autores principales: | , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
May2015
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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=115925105&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 115925105 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: May2015 vid: 39 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 115925105 115925105 115925105 10.1007/s10916-015-0231-5 115925105 ppf: 1 ppct: 13 formats: fmt: @attributes: type: P tig: atl: Shape and Texture Based Novel Features for Automated Juxtapleural Nodule Detection in Lung CTs. aug: au: Taşcı, Erdal Uğur, Aybars affil: Department of Computer Engineering, Ege University, Izmir Turkey sug: subj: Lung Neoplasms Diagnosis Lung Neoplasms Radiography Diagnostic Imaging Evaluation Tomography, X-Ray Computed Evaluation Human Receptors, Pattern Recognition Image Processing, Computer Assisted Artificial Intelligence DICOM Funding Source ab: Lung cancer is one of the types of cancer with highest mortality rate in the world. In case of early detection and diagnosis, the survival rate of patients significantly increases. In this study, a novel method and system that provides automatic detection of juxtapleural nodule pattern have been developed from cross-sectional images of lung CT (Computerized Tomography). Shape-based and both shape and texture based 7 features are contributed to the literature for lung nodules. System that we developed consists of six main stages called preprocessing, lung segmentation, detection of nodule candidate regions, feature extraction, feature selection (with five feature ranking criteria) and classification. LIDC dataset containing cross-sectional images of lung CT has been utilized, 1410 nodule candidate regions and 40 features have been extracted from 138 cross-sectional images for 24 patients. Experimental results for 10 classifiers are obtained and presented. Adding our derived features to known 33 features has increased nodule recognition performance from 0.9639 to 0.9679 AUC value on generalized linear model regression (GLMR) for 22 selected features and being reached one of the most successful results in the literature. 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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