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Proceedings Paper

Analysis of parenchymal texture properties in breast tomosynthesis images
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Paper Abstract

We have analyzed breast parenchymal texture in tomosynthesis images. Tomosynthesis is a novel x-ray imaging modality in which 3D images of the breast are reconstructed from multiple 2D x-ray source projection images acquired by varying the angle of the x-ray tube. Our ultimate goal is to examine the correlation between tomosynthesis texture descriptors and breast cancer risk. As a first step, we investigated the effect of tomosynthesis acquisition parameters on texture in the source projection images; this avoids the influence of the reconstruction algorithm. We computed statistical texture descriptors which have been shown in the literature to be highly indicative of breast cancer risk. We compared skewness, coarseness, and contrast computed from the central source projection images and the corresponding mammograms. Our analysis showed that differences exist between mammographic and tomosynthetic texture in projection images. Retroareolar ROIs in tomosynthesis images appeared to be less skewed with lower coarseness and higher contrast measures compared to mammograms; however, corresponding texture descriptors for tomosynthesis and mammography are correlated. Examination of the ROIs demonstrates that the texture in tomosynthesis source projections visually differs from the x-ray mammograms. We attribute this observation to acquisition differences, including radiation dose, compression force, and x-ray scatter. As with mammography, tomosynthesis parenchymal texture is related to the Gail-model cancer risk. Although preliminary, we believe that texture analysis of 3D breast tomosynthesis images will ultimately yield more accurate and precise measures of risk.

Paper Details

Date Published: 29 March 2007
PDF: 10 pages
Proc. SPIE 6514, Medical Imaging 2007: Computer-Aided Diagnosis, 651417 (29 March 2007); doi: 10.1117/12.713851
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. 6514:
Medical Imaging 2007: Computer-Aided Diagnosis
Maryellen L. Giger; Nico Karssemeijer, Editor(s)

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