Semi-automated segmentation and classification of digital breast tomosynthesis reconstructed images

UMMS Affiliation

Department of Radiology



Document Type


Medical Subject Headings

Adipose Tissue; Algorithms; Anisotropy; Breast; Cluster Analysis; Diffusion; Equipment Design; Female; Fuzzy Logic; Humans; Image Processing, Computer-Assisted; Light; Magnetic Resonance Imaging; Mammography; Muscles; Scattering, Radiation; Skin; Spectroscopy, Near-Infrared; X-Rays


Bioimaging and Biomedical Optics | Biomedical Devices and Instrumentation | Diagnosis | Investigative Techniques | Radiology


Digital breast tomosynthesis (DBT) is a limited-angle tomographic x-ray imaging technique that reduces the effect of tissue superposition observed in planar mammography. An integrated imaging platform that combines DBT with near infrared spectroscopy (NIRS) to provide co-registered anatomical and functional imaging is under development. Incorporation of anatomic priors can benefit NIRS reconstruction. In this work, we provide a segmentation and classification method to extract potential lesions, as well as adipose, fibroglandular, muscle and skin tissue in reconstructed DBT images that serve as anatomic priors during NIRS reconstruction. The method may also be adaptable for estimating tumor volume, breast glandular content, and for extracting lesion features for potential application to computer aided detection and diagnosis.

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Citation: Conf Proc IEEE Eng Med Biol Soc. 2011;2011:6188-91. doi: 10.1109/IEMBS.2011.6091528. Link to article on publisher's site

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