Pulmonary Nodule Detection Model Based on SVM and CT Image Feature-Level Fusion with Rough Sets.
In order to improve the detection accuracy of pulmonary nodules in CT image, considering two problems of pulmonary nodules detection model, including unreasonable feature structure and nontightness of feature representation, a pulmonary nodules detection algorithm is proposed based on SVM and CT ima...
| Publicado en: | BioMed Research International Vol. 2016; pp. 1 - 14 |
|---|---|
| Autores principales: | , , , |
| Formato: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
9/18/2016
|
| 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=118163215&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 118163215 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 9/18/2016 vid: 2016 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 118163215 118163215 118163215 10.1155/2016/8052436 118163215 ppf: 1 ppct: 13 formats: fmt: @attributes: type: P tig: atl: Pulmonary Nodule Detection Model Based on SVM and CT Image Feature-Level Fusion with Rough Sets. aug: au: Zhou, Tao Lu, Huiling Zhang, Junjie Shi, Hongbin affil: School of Science, Ningxia Medical University, Ningxia, Yinchuan 750004, China sug: subj: Solitary Pulmonary Nodule Diagnosis Tomography, X-Ray Computed Algorithms Utilization Lung Radiography Radiographic Image Interpretation, Computer-Assisted Models, Statistical Instrument Scaling Funding Source ab: In order to improve the detection accuracy of pulmonary nodules in CT image, considering two problems of pulmonary nodules detection model, including unreasonable feature structure and nontightness of feature representation, a pulmonary nodules detection algorithm is proposed based on SVM and CT image feature-level fusion with rough sets. Firstly, CT images of pulmonary nodule are analyzed, and 42-dimensional feature components are extracted, including six new 3-dimensional features proposed by this paper and others 2-dimensional and 3-dimensional features. Secondly, these features are reduced for five times with rough set based on feature-level fusion. Thirdly, a grid optimization model is used to optimize the kernel function of support vector machine (SVM), which is used as a classifier to identify pulmonary nodules. Finally, lung CT images of 70 patients with pulmonary nodules are collected as the original samples, which are used to verify the effectiveness and stability of the proposed model by four groups’ comparative experiments. The experimental results show that the effectiveness and stability of the proposed model based on rough set feature-level fusion are improved in some degrees. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|