Perceptual sensitivity to anatomical background statistics in mammography

项目摘要/摘要 众所周知，医学图像中的正常解剖结构可以掩盖疾病的存在。然而， 这一过程还没有得到很好的理解。问题的一部分是我们缺乏对相关统计数据的了解 描述图像统计的感知效果的描述符。而图像采集噪声在很大程度上 背景解剖学以其二阶矩(功率谱或协方差矩阵)为特征，具有 需要高阶统计量的复杂结构--以及对其知觉相关性的理解 -充分描述。这是一个重要的限制，因为“通读”这一背景是至关重要的 是许多临床任务的组成部分。从统计学意义上讲，通读背景资料意味着利用 为隔离疾病而呈现的正常解剖结构中的冗余 流程。在筛查乳房X光检查中，背景特征的必要性得到了很好的认识，我们的 焦点，因为筛查乳房X光检查通常包括通过BIRADS对背景进行评估 密度分数。然而，这一分数作为统计描述符的效用有限。 这个项目的基础是将一种成功的方法从基础视觉科学转化为医学 成像，以便识别医学图像的相关高阶统计特性及其 感知上的影响。在该方法中，使用一组局部图像统计(共现概率)来 为合成视觉纹理的统计结构及其局部特征(例如 边)。对局部特征的感知敏感度可以通过这一点简明地描述和建模 字母表，并已表明对这些元素的敏感度与它们的信息量在 自然风光。这激励了我们的总体方法，以及我们研究计划的许多细节。 我们的计划是在目标1中开发算法，有选择地改变(或增加或减少)乳房X光照片的共现统计数据，同时保留它们的一般背景外观。子目标 探索四种策略，建立在傅里叶域方法的基础上，并对其进行原则证明。 然后，目标2将使用这些图像来评估感知敏感度。目标2A将发展心理物理 范例。AIMS 2B-D将确定在以前的合成研究中确定的原则是否 视觉纹理(符号不变性、近似比例不变性和二次组合)扩展到 医学图像，因为这将使对感知敏感性的全面而简洁的描述成为可能。我们会 使用全视野数字乳房X光照片数据库来实现这些目标。 预计该项目将产生一种调制高阶统计特性的有效方法 乳房X光照片和基线数据对这些调制的感知敏感度。这些发现将 加深我们对正常解剖结构如何影响筛查统计特性的理解 乳房X光检查，并为我们提供了关于正常解剖统计如何影响感知的有价值的基线数据。

Project Summary/Abstract It is well known that normal anatomy in a medical image can mask the presence of disease. However this process is not well understood. Part of the problem is that we lack knowledge of the relevant statistical descriptors that characterize perceptual effects of image statistics. While image acquisition noise is largely characterized by its second moments (power-spectrum or covariance matrix), background anatomy has a complex structure that requires higher-order statistics – and an understanding of their perceptual relevance –to characterize fully. This is an important limitation because reading “through” this background is a critical component of many clinical tasks. In a statistical sense, reading through the background means exploiting redundancies in the presentation of normal anatomical structures for the purpose of isolating disease processes. The need for background characterization is well recognized in screening mammography, our focus, as screening mammography typically includes an assessment of the background via the BIRADS density score. However, this score has limited utility as a statistical descriptor. The basis for this project is to translate a successful approach from basic vision science to medical imaging, in order to identify the relevant high-order statistical properties of medical images and their perceptual impact. In this approach, a set of local image statistics (co-occurrence probabilities) are used to build an ”alphabet” for the statistical structure of synthetic visual textures and their local features (such as edges). Perceptual sensitivities to local features can be concisely characterized and modeled via this alphabet, and it has been shown that sensitivity to these elements is matched to their informativeness in natural scenes. This motivates our general approach, and many specifics of our research plan. Our plan is to develop algorithms in Aim 1 that selectively alter (either increase or decrease) the co-occurrence statistics of mammograms, while retaining their general background appearance. The sub-aims explore four strategies, building on a Fourier domain approach for which a proof-of-principle is in hand. Then, Aim 2 will use these images to assess perceptual sensitivity. Aim 2A will develop the psychophysical paradigm. Aims 2B-D will determine whether the principles identified in previous studies of synthetic visual textures (sign-invariance, approximate scale-invariance, and quadratic combination) extend to medical images, as this will enable a comprehensive yet concise description of perceptual sensitivity. We will pursue these aims using a database of full-field digital mammograms. The project is expected to yield a validated approach for modulating high-order statistical properties of mammograms and baseline data of perceptual sensitivity to these modulations. These findings will improve our understanding how normal anatomy impacts the statistical properties of screening mammograms, and give us valuable baseline data on how the statistics of normal anatomy affect perception.