Cone Beam Breast CT noise reduction using 3D adaptive Gaussian filtering

Article type: Research Article

Authors: Zhang, Xiaohua | Ning, Ruola^; | Yang, Dong

Affiliations: Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, USA | Department of Imaging Sciences, University of Rochester, Rochester, NY, USA

Note: [] Corresponding author. Tel.: +1 585 275 5986; Fax: +1 585 273 1033; E-mail: xiaohua_zhang@urmc.rochester.edu

Abstract: In Cone Beam Breast CT (CBBCT) imaging, noise causes degradation of three dimensional breast images, impeding correct diagnosis of breast cancer. Within Feldkamp's cone beam reconstruction framework, applying weighted reconstruction filters to the projection images after pre-processing procedures has long been used to reduce noise and improve image quality. However, CBBCT noise is distributed across frequencies along with the useful signal. Various reconstruction filters working in the frequency domain suppress noise as well as the edge detail signal. Based on fuzzy c-means clustering and the two-dimensional histogram analysis of a large number of clinical CBBCT data, we managed to discriminate fatty stroma, glandular tissues and the transition areas between these tissues by the local mean and standard deviation values. We also proposed a three-dimensional Gaussian filtering scheme to reduce the noise in 3D reconstructed images adaptively without much blurring of detail signal.

Keywords: Noise reduction, 3D adaptive Gaussian filtering, CBBCT

DOI: 10.3233/XST-2009-0232

Journal: Journal of X-Ray Science and Technology, vol. 17, no. 4, pp. 319-333, 2009