| pubmed-article:19506953 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:19506953 | lifeskim:mentions | umls-concept:C0441635 | lld:lifeskim |
| pubmed-article:19506953 | lifeskim:mentions | umls-concept:C1659543 | lld:lifeskim |
| pubmed-article:19506953 | lifeskim:mentions | umls-concept:C0038215 | lld:lifeskim |
| pubmed-article:19506953 | lifeskim:mentions | umls-concept:C0449445 | lld:lifeskim |
| pubmed-article:19506953 | pubmed:issue | 5 | lld:pubmed |
| pubmed-article:19506953 | pubmed:dateCreated | 2010-10-6 | lld:pubmed |
| pubmed-article:19506953 | pubmed:abstractText | Studies reported in the literature indicate that the increase in the breast density is one of the strongest indicators of developing breast cancer. In this paper, we present an approach to automatically evaluate the density of a breast by segmenting its internal parenchyma in either fatty or dense class. Our approach is based on a statistical analysis of each pixel neighbourhood for modelling both tissue types. Therefore, we provide connected density clusters taking the spatial information of the breast into account. With the aim of showing the robustness of our approach, the experiments are performed using two different databases: the well-known Mammographic Image Analysis Society digitised database and a new full-field digital database of mammograms from which we have annotations provided by radiologists. Quantitative and qualitative results show that our approach is able to correctly detect dense breasts, segmenting the tissue type accordingly. | lld:pubmed |
| pubmed-article:19506953 | pubmed:language | eng | lld:pubmed |
| pubmed-article:19506953 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:19506953 | pubmed:citationSubset | IM | lld:pubmed |
| pubmed-article:19506953 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:19506953 | pubmed:month | Oct | lld:pubmed |
| pubmed-article:19506953 | pubmed:issn | 1618-727X | lld:pubmed |
| pubmed-article:19506953 | pubmed:author | pubmed-author:MartíJoanJ | lld:pubmed |
| pubmed-article:19506953 | pubmed:author | pubmed-author:PontJosepJ | lld:pubmed |
| pubmed-article:19506953 | pubmed:author | pubmed-author:OliverArnauA | lld:pubmed |
| pubmed-article:19506953 | pubmed:author | pubmed-author:FreixenetJord... | lld:pubmed |
| pubmed-article:19506953 | pubmed:author | pubmed-author:LladóXavierX | lld:pubmed |
| pubmed-article:19506953 | pubmed:author | pubmed-author:PérezElsaE | lld:pubmed |
| pubmed-article:19506953 | pubmed:author | pubmed-author:DentonErika... | lld:pubmed |
| pubmed-article:19506953 | pubmed:issnType | Electronic | lld:pubmed |
| pubmed-article:19506953 | pubmed:volume | 23 | lld:pubmed |
| pubmed-article:19506953 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:19506953 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:19506953 | pubmed:pagination | 527-37 | lld:pubmed |
| pubmed-article:19506953 | pubmed:dateRevised | 2011-10-3 | lld:pubmed |
| pubmed-article:19506953 | pubmed:meshHeading | pubmed-meshheading:19506953... | lld:pubmed |
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| pubmed-article:19506953 | pubmed:meshHeading | pubmed-meshheading:19506953... | lld:pubmed |
| pubmed-article:19506953 | pubmed:meshHeading | pubmed-meshheading:19506953... | lld:pubmed |
| pubmed-article:19506953 | pubmed:meshHeading | pubmed-meshheading:19506953... | lld:pubmed |
| pubmed-article:19506953 | pubmed:meshHeading | pubmed-meshheading:19506953... | lld:pubmed |
| pubmed-article:19506953 | pubmed:meshHeading | pubmed-meshheading:19506953... | lld:pubmed |
| pubmed-article:19506953 | pubmed:year | 2010 | lld:pubmed |
| pubmed-article:19506953 | pubmed:articleTitle | A statistical approach for breast density segmentation. | lld:pubmed |
| pubmed-article:19506953 | pubmed:affiliation | Department of Computer Architecture and Technology, IIiA-IdIBGi, University of Girona, Campus Montilivi, Ed. P-IV, 17071 Girona, Spain. aoliver@eia.udg.edu | lld:pubmed |
| pubmed-article:19506953 | pubmed:publicationType | Journal Article | lld:pubmed |
| pubmed-article:19506953 | pubmed:publicationType | Research Support, Non-U.S. Gov't | lld:pubmed |
