Non-Gaussian statistical properties of breast images
Non-Gaussian statistical properties of breast images
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DOI：
10.1118/1.4761869
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发表时间：
2012-11-01
期刊：
影响因子：
3.8
通讯作者：
Boone, John M.
中科院分区：
文献类型：
--
作者：
Abbey, Craig K.;Nosrateih, Anita;Boone, John M.
目的：几项研究表明，X射线乳腺图像的功率谱很好地描述了在较低的频率，其中解剖变异占主导地位的幂律。然而，通过眼睛容易将利用该谱从高斯处理生成的图像与实际乳房组织的图像区分开。这表明，乳房X线照片的高阶非高斯统计特性容易被视觉系统访问。作者的目的是量化和表征非高斯统计特性的乳房图像的数字乳房X线照片，不同的成像方式，和乳房density.Methods的处理的影响：要量化非高斯统计特性，作者认为直方图的过滤器响应从内部的乳房图像，具有类似的属性在早期的视觉系统中的感受野。它们通过直方图与最佳拟合高斯分布相比的相对熵来量化高斯分布的偏离。该熵通过最佳拟合拉普拉斯分布的相对熵归一化为他们称为拉普拉斯分数熵（LFE）的度量。他们在筛选时召回的26名患者中测试了LFE，这些患者具有可用的全视野数字乳腺X射线摄影（FFDM）、数字乳腺断层合成摄影（DBT）和专用乳腺CT（bCT）图像以及乳腺密度评分和活检结果。FFDM中的LFE研究比较了来自设备的原始“待处理”传输数据与对数转换密度估计值以及经处理的“待处理”传输数据。“显示”数据显示，处理乳房X线摄影图像数据增强了图像的非高斯内容。检查的方法使用高斯过程与幂律功率谱显示相对较小的偏差，从有限范围内的区域使用的利益。第二项研究比较了FFDM、DBT和bCT模式的LFE，结果表明，每种模式都最大化了不同空间频率范围内图像的非高斯内容。FFDM在高空间频率（> 0.7 mm（-1））下是最佳的，DBT在中等范围频率（0.3-0.7 mm（-1））下是最佳的，而bCT在低空间频率（< 0.3 mm（-1））下是最佳的。第三项关于FFDM和bCT中乳腺密度的研究表明，LFE通常从低密度到中等密度略有上升，然后在较高密度时显著下降福尔斯。在类似于早期视觉系统的感受野的伽柏滤波器的响应中显现的乳房图像中的非高斯统计结构取决于如何处理图像数据，用于获取图像的模态，以及被成像的乳房组织的密度。更高的LFE对应于图像处理和3D成像的预期改进。(C)2012年美国医学物理学家协会。[http：//dx.doi.org/10.1118/1.4761869]
Purpose: Several studies have shown that the power spectrum of x-ray breast images is well described by a power-law at lower frequencies where anatomical variability dominates. However, an image generated from a Gaussian process with this spectrum is easily distinguished from an image of actual breast tissue by eye. This demonstrates that higher order non-Gaussian statistical properties of mammograms are readily accessible to the visual system. The authors' purpose is to quantify and characterize non-Gaussian statistical properties of breast images as influenced by processing of a digital mammogram, different imaging modalities, and breast density.Methods: To quantify non-Gaussian statistical properties, the authors consider histograms of filter responses from the interior of a breast image that have similar properties to receptive fields in the early visual system. They quantify departure from a Gaussian distribution by the relative entropy of the histogram compared to a best-fit Gaussian distribution. This entropy is normalized by the relative entropy of a best-fit Laplacian distribution into a measure they refer to as Laplacian fractional entropy (LFE). They test the LFE on a set of 26 patients recalled at screening for which they have available full-field digital mammography (FFDM), digital breast tomosynthesis (DBT), and dedicated breast CT (bCT) images as well as breast density scores and biopsy results.Results: A study of LFE in FFDM comparing the raw "for-processing" transmission data from the device to log-converted density estimates and the processed "for-display" data shows that processing mammographic image data enhances the non-Gaussian content of the image. A check of the methodology using a Gaussian process with a power-law power spectrum shows relatively little bias from the finite extent of the region of interests used. A second study comparing LFE across FFDM, DBT, and bCT modalities shows that each maximized the non-Gaussian content of the image for different ranges of spatial frequency. FFDM is optimal at high spatial frequencies (> 0.7 mm(-1)), DBT is optimal at mid-range frequencies (0.3-0.7 mm(-1)), and bCT is optimal at low spatial frequency (< 0.3 mm(-1)). A third study of breast density in FFDM and bCT shows that LFE generally rises slightly going from the low-to moderate density, and then falls considerably at higher densities.Conclusions: Non-Gaussian statistical structure in breast images that is manifest in the responses of Gabor filters similar to receptive fields of the early visual system is dependent on how the image data are processed, the modality used to acquire the image, and the density of the breast tissue being imaged. Higher LFE corresponds with expected improvements from image processing and 3D imaging. (C) 2012 American Association of Physicists in Medicine. [http://dx.doi.org/10.1118/1.4761869]