A new approach for clustered MCs classification with sparse features learning and TWSVM.
In digital mammograms, an early sign of breast cancer is the existence of microcalcification clusters (MCs), which is very important to the early breast cancer detection. In this paper, a new approach is proposed to classify and detect MCs. We formulate this classification problem as sparse feature...
| Publicado en: | Scientific World Journal pp. 970287 - 970288 |
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| Autor principal: | |
| Formato: | research Journal Article |
| Publicado: |
Wiley-Blackwell
2014
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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=103932295&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 103932295 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1537744X 1BX5 jtl: Scientific World Journal issn: 1537744X maglogo: N pubinfo: dt: 2014 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 103932295 NLM24764773 2012559265 10.1155/2014/970287 NLM24764773 PMC3934082 103932295 ppf: 970287 ppct: 1 formats: tig: atl: A new approach for clustered MCs classification with sparse features learning and TWSVM. aug: au: Zhang, Xin-Sheng affil: School of Management, Xi'an University of Architecture and Technology, Xi'an, Shaanxi 710055, China. sug: subj: Breast Neoplasms Radiography Calcinosis Pathology Calcinosis Radiography Mammography Methods Information Science Methods Algorithms Female ROC Curve Radiographic Image Interpretation, Computer-Assisted Methods Human Female ab: In digital mammograms, an early sign of breast cancer is the existence of microcalcification clusters (MCs), which is very important to the early breast cancer detection. In this paper, a new approach is proposed to classify and detect MCs. We formulate this classification problem as sparse feature learning based classification on behalf of the test samples with a set of training samples, which are also known as a "vocabulary" of visual parts. A visual information-rich vocabulary of training samples is manually built up from a set of samples, which include MCs parts and no-MCs parts. With the prior ground truth of MCs in mammograms, the sparse feature learning is acquired by the l(P)-regularized least square approach with the interior-point method. Then we designed the sparse feature learning based MCs classification algorithm using twin support vector machines (TWSVMs). To investigate its performance, the proposed method is applied to DDSM datasets and compared with support vector machines (SVMs) with the same dataset. Experiments have shown that performance of the proposed method is more efficient or better than the state-of-art methods. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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