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Proceedings Paper

Nonlinear dual-spectral image fusion for improving cone-beam-CT-based breast cancer diagnosis
Author(s): Zikuan Chen; Ruola Ning; David Conover; Kathleen Willison
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Paper Abstract

Cone-beam breast computed tomography (CB Breast CT) can easily detect micro-calcifications and distinguish fat and glandular tissues from normal breast tissue. However, it may be a challenging task for CB Breast CT to distinguish benign from malignant tumors because of the subtle difference in x-ray attenuation. Due to the use of polyenergetic x-ray source, the x-ray and tissue interaction exhibits energy-dependent attenuation behavior, a phenomenon that, to date, has not been used for breast tissue characterization. We will exploit this spectral nature by equipping our CB Breast CT with dual-spectral imaging. The dual-spectral cone-beam scanning produces two spectral image datasets, from which we propose a nonlinear dual-spectral image fusion scheme to combine them into a single dataset, thereby incorporating the spectral information. In implementation, we will perform dual-spectral image fusion through a bi-variable polynomial that can be established by applying dual-spectral imaging to a reference material (with eight different thicknesses). From the fused dataset, we can reconstruct a volume, called a reference-equivalent volume or a fusion volume. By selecting the benign tissue as a reference material, we obtain a benign-equivalent volume. Likewise, we obtain a malignant-equivalent volume as well. In the pursuit of the discrimination of benign versus malignant tissues in a breast image, we perform intra-image as well as inter-image processing. The intra-image processing is an intensity transformation imposed only to a tomographic breast image itself, while the inter-image processing is exerted on two tomographic images extracted from two volumes. The nonlinear fusion scheme possesses these properties: 1) no noise magnification; 2) no feature dimensionality problem, and 3) drastic enhancement among specific features offered by nonlinear mapping. Its disadvantage lies in the possible misinterpretation resulting from nonlinear mapping.

Paper Details

Date Published: 2 March 2006
PDF: 12 pages
Proc. SPIE 6142, Medical Imaging 2006: Physics of Medical Imaging, 61422P (2 March 2006); doi: 10.1117/12.652715
Show Author Affiliations
Zikuan Chen, Univ. of Rochester (United States)
Ruola Ning, Univ. of Rochester (United States)
David Conover, Univ. of Rochester (United States)
Kathleen Willison, Univ. of Rochester (United States)

Published in SPIE Proceedings Vol. 6142:
Medical Imaging 2006: Physics of Medical Imaging
Michael J. Flynn; Jiang Hsieh, Editor(s)

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