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Proceedings Paper

Texture in digital breast tomosynthesis: a comparison between mammographic and tomographic characterization of parenchymal properties
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Paper Abstract

Studies have demonstrated a relationship between mammographic texture and breast cancer risk. To date, texture analysis has been limited by tissue superimposition in mammography. Digital Breast Tomosynthesis (DBT) is a novel x-ray imaging modality in which 3D images of the breast are reconstructed from a limited number of source projections. Tomosynthesis alleviates the effect of tissue superimposition and offers the ability to perform tomographic texture analysis; having the potential to ultimately yield more accurate measures of risk. In this study, we analyzed texture in DBT and digital mammography (DM). Our goal was to compare tomographic versus mammographic texture characterization and evaluate the robustness of texture descriptors in reflecting characteristic parenchymal properties. We analyzed DBT and DM images from 40 women with recently detected abnormalities and/or previously diagnosed breast cancer. Texture features, previously shown to correlate with risk, were computed from the retroareolar region. We computed the texture correlation between (i) the DBT and DM, and (ii) between contralateral and ipsilateral breasts. The effect of the gray-level quantization on the observed correlations was investigated. Low correlation was detected between DBT and DM features. The correlation between contralateral and ipsilateral breasts was significant for both modalities, and overall stronger for DBT. We observed that the selection of the gray-level quantization algorithm affects the detected correlations. The strong correlation between contralateral and ipsilateral breasts supports the hypothesis that parenchymal properties appear to be inherent in an individual woman; the texture of the unaffected breast could potentially be used as a marker of risk.

Paper Details

Date Published: 17 March 2008
PDF: 11 pages
Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 69150A (17 March 2008); doi: 10.1117/12.773144
Show Author Affiliations
Despina Kontos, Univ. of Pennsylvania (United States)
Predrag R. Bakic, Univ. of Pennsylvania (United States)
Andrew D. A. Maidment, Univ. of Pennsylvania (United States)


Published in SPIE Proceedings Vol. 6915:
Medical Imaging 2008: Computer-Aided Diagnosis
Maryellen L. Giger; Nico Karssemeijer, Editor(s)

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