Mass segmentation of mammograms using Markov models associated with constrained clustering.
In this paper, we propose four variants of the Markov random field model by using constrained clustering for breast mass segmentation. These variants were tested with a set of images extracted from a public database. The obtained results have shown that the proposed variants, which allow to include...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 58; no. 10; pp. 2475 - 2496 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Oct2020
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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=145758134&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 145758134 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Oct2020 vid: 58 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 145758134 145758134 NLM32780256 10.1007/s11517-020-02221-w NLM32780256 145758134 ppf: 2475 ppct: 21 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Mass segmentation of mammograms using Markov models associated with constrained clustering. aug: au: Cruz-Barbosa, Raúl Hernández-Hernández, Saiveth Sucar, Luis Enrique affil: Applied Artificial Intelligence Laboratory, Computer Science Institute, Universidad Tecnológica de la Mixteca, Huajuapan, Oaxaca, México sug: subj: Mammography Methods Breast Neoplasms Radiographic Image Interpretation, Computer-Assisted Methods Probability Cluster Analysis Female Resource Databases Algorithms Ferrans and Powers Quality of Life Index Female ab: In this paper, we propose four variants of the Markov random field model by using constrained clustering for breast mass segmentation. These variants were tested with a set of images extracted from a public database. The obtained results have shown that the proposed variants, which allow to include additional information in the form of constraints to the clustering process, present better visual segmentation results than the original model, as well as a lower final energy which implies a better quality in the final segmentation. Specifically, the centroid initialization method used by our variants allows us to locate about 90% of the regions of interest that contain a mass, which subsequently with the pairwise constraints helped us recover a maximum of 93% of the masses. The segmentation results are also quantitatively evaluated using three supervised segmentation measures. These measures show that the mass segmentation quality of the proposed variants, considering the breast density level, is consistent with the corresponding segmentation annotated by specialized radiologists. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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