Classification of breast masses using selected shape, edge-sharpness, and texture features with linear and kernel-based classifiers.
Breast masses due to benign disease and malignant tumors related to breast cancer differ in terms of shape, edge-sharpness, and texture characteristics. In this study, we evaluate a set of 22 features including 5 shape factors, 3 edge-sharpness measures, and 14 texture features computed from 111 reg...
| Publicado en: | Journal of Digital Imaging Vol. 21; no. 2; pp. 153 - 170 |
|---|---|
| Autores principales: | , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2008
|
| 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=105781006&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105781006 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2008 vid: 21 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 105781006 105781006 2009933338 NLM18306000 105781006 ppf: 153 ppct: 17 formats: fmt: @attributes: type: P tig: atl: Classification of breast masses using selected shape, edge-sharpness, and texture features with linear and kernel-based classifiers. aug: au: Mu T Nandi AK Rangayyan RM affil: Department of Electrical Engineering and Electronics, The University of Liverpool, Brownlow Hill, L69 3GJ, Liverpool, UK. sug: subj: Breast Neoplasms Classification Breast Neoplasms Diagnosis Breast Neoplasms Radiography Diagnosis, Computer Assisted Mammography Algorithms Confidence Intervals Data Analysis Software Evaluation Research Funding Source ROC Curve Software T-Tests Human ab: Breast masses due to benign disease and malignant tumors related to breast cancer differ in terms of shape, edge-sharpness, and texture characteristics. In this study, we evaluate a set of 22 features including 5 shape factors, 3 edge-sharpness measures, and 14 texture features computed from 111 regions in mammograms, with 46 regions related to malignant tumors and 65 to benign masses. Feature selection is performed by a genetic algorithm based on several criteria, such as alignment of the kernel with the target function, class separability, and normalized distance. Fisher's linear discriminant analysis, the support vector machine (SVM), and our strict two-surface proximal (S2SP) classifier, as well as their corresponding kernel-based nonlinear versions, are used in the classification task with the selected features. The nonlinear classification performance of kernel Fisher's discriminant analysis, SVM, and S2SP, with the Gaussian kernel, reached 0.95 in terms of the area under the receiver operating characteristics curve. The results indicate that improvement in classification accuracy may be gained by using selected combinations of shape, edge-sharpness, and texture features. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|