Breast Density Analysis Using an Automatic Density Segmentation Algorithm.
Breast density is a strong risk factor for breast cancer. In this paper, we present an automated approach for breast density segmentation in mammographic images based on a supervised pixel-based classification and using textural and morphological features. The objective of the paper is not only to s...
| Publicado en: | Journal of Digital Imaging Vol. 28; no. 5; pp. 604 - 613 |
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| Autores principales: | , , , , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2015
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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=109465613&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109465613 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2015 vid: 28 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 109465613 109465613 109465613 10.1007/s10278-015-9777-5 NLM25720749 PMC4570891 109465613 ppf: 604 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Breast Density Analysis Using an Automatic Density Segmentation Algorithm. aug: au: Oliver, Arnau Tortajada, Meritxell Lladó, Xavier Freixenet, Jordi Ganau, Sergi Tortajada, Lidia Vilagran, Mariona Sentís, Melcior Martí, Robert affil: Department of Computer Architecture and Technology, University of Girona, 17071 Girona Spain sug: subj: Breast Anatomy and Histology Mammography Breast Neoplasms Radiography Radiographic Image Interpretation, Computer-Assisted Methods Algorithms Spain Evaluation Research Paired T-Tests Regression Prospective Studies Descriptive Statistics P-Value Human Funding Source ab: Breast density is a strong risk factor for breast cancer. In this paper, we present an automated approach for breast density segmentation in mammographic images based on a supervised pixel-based classification and using textural and morphological features. The objective of the paper is not only to show the feasibility of an automatic algorithm for breast density segmentation but also to prove its potential application to the study of breast density evolution in longitudinal studies. The database used here contains three complete screening examinations, acquired 2 years apart, of 130 different patients. The approach was validated by comparing manual expert annotations with automatically obtained estimations. Transversal analysis of the breast density analysis of craniocaudal (CC) and mediolateral oblique (MLO) views of both breasts acquired in the same study showed a correlation coefficient of ρ = 0.96 between the mammographic density percentage for left and right breasts, whereas a comparison of both mammographic views showed a correlation of ρ = 0.95. A longitudinal study of breast density confirmed the trend that dense tissue percentage decreases over time, although we noticed that the decrease in the ratio depends on the initial amount of breast density. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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