Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, USA.
J Xray Sci Technol. 2009;17(4):319-33. doi: 10.3233/XST-2009-0232.
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.
在锥形束乳腺 CT（CBBCT）成像中，噪声会导致三维乳腺图像质量下降，从而影响乳腺癌的正确诊断。在 Feldkamp 的锥形束重建框架中，在预处理步骤之后，将加权重建滤波器应用于投影图像，以减少噪声并提高图像质量。然而，CBBCT 噪声与有用信号一起沿频率分布。各种在频域中工作的重建滤波器会同时抑制噪声和边缘细节信号。基于模糊 c-均值聚类和对大量临床 CBBCT 数据的二维直方图分析，我们通过局部均值和标准偏差值成功区分了脂肪基质、腺体组织以及这些组织之间的过渡区域。我们还提出了一种三维高斯滤波方案，以在不明显模糊细节信号的情况下自适应地减少 3D 重建图像中的噪声。
