A quantitative analysis of breast densities using cone beam CT images

Ricardo Betancourt Benítez; Ruola Ning; David Conover; Shaohua Liu

doi:10.1117/12.811075

3 March 2009 A quantitative analysis of breast densities using cone beam CT images

Ricardo Betancourt Benítez, Ruola Ning, David Conover, Shaohua Liu

Proceedings Volume 7260, Medical Imaging 2009: Computer-Aided Diagnosis; 72602C (2009) https://doi.org/10.1117/12.811075
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

Duct patterns are formed by desmoplastic reactions as most breast carcinomas are. Hence, it has been suggested that the denser a breast is, the higher the likelihood to develop breast cancer. Consequently, breast density has been one of the suggested parameters to estimate the risk to develop breast cancer. Currently, the main technique to evaluate breast densities is through mammograms. However, mammograms have the disadvantage of displaying overlapping structures within the breast. Although there are efficient techniques to obtain breast densities from mammograms, mammography can only provide a rough estimate because of the overlapping breast tissue. In this study, cone beam CT images were utilized to evaluate the breast density of sixteen breast images. First, a breast phantom with known volumes representing fatty, glandular and calcified tissues was designed to calibrate the system. Since cone beam CT provides 3D-isotropic resolution images throughout the field of view, the issue of overlapping structures disappears, allowing greater accuracy in evaluating the volumes of each different part of the phantom. Then, using cone beam CT breast images, the breast density of eight patients was evaluated using a semi-automatic segmentation algorithm that differentiates between fatty, glandular and calcified tissues. The results demonstrated that cone beam CT images provide a better tool to evaluate the breast density of the whole breast more accurately. The results also demonstrated that using this semi-automatic segmentation algorithm improves the efficiency of classifying the breast into the four classifications as recommended by the American College of Radiology.

Citation Download Citation

Ricardo Betancourt Benítez, Ruola Ning, David Conover, and Shaohua Liu "A quantitative analysis of breast densities using cone beam CT images", Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72602C (3 March 2009); https://doi.org/10.1117/12.811075

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