An Automated System for Classification of Radiographs of the Breast.
The article discusses the basic principles of design and testing of an automated system for classification of radiographs of the breast. The principle of operation of the automated system is based on the segmentation of radiographs into rectangular areas with their subsequent deformation according t...
| Publicado en: | Biomedical Engineering Vol. 53; no. 6; pp. 425 - 429 |
|---|---|
| Autores principales: | , , , , |
| Formato: | diagnostic images tables/charts Journal Article |
| Publicado: |
Springer Nature
Mar2020
|
| 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=148389342&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 148389342 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00063398 N96 jtl: Biomedical Engineering issn: 00063398 maglogo: N pubinfo: dt: Mar2020 vid: 53 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 148389342 148389342 148389342 10.1007/s10527-020-09957-7 148389342 ppf: 425 ppct: 4 formats: tig: atl: An Automated System for Classification of Radiographs of the Breast. aug: au: Dabagov, A. R. Gorbunov, V. A. Filist, S. A. Malyutina, I. A. Kondrashov, D. S. affil: Medical Technologies Ltd., Moscow, Russia sug: subj: Mammography Classification Automation, Laboratory Radiography Neural Networks (Computer) Algorithms Magnetic Resonance Imaging Software ab: The article discusses the basic principles of design and testing of an automated system for classification of radiographs of the breast. The principle of operation of the automated system is based on the segmentation of radiographs into rectangular areas with their subsequent deformation according to threshold-type criteria of homogeneity. Classification of the selected segments is carried out by neural network classifiers operating in the space of information-bearing features that is built using multi-method algorithms. The software is implemented in MATLAB 2018b. The quality of classification using the developed software was tested on control samples. pubtype: Academic Journal doctype: diagnostic images tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|