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首页> 外文期刊>IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control >A discrete-scatterer model for ultrasonic images of rough surfaces
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A discrete-scatterer model for ultrasonic images of rough surfaces

机译：粗糙表面超声图像的离散散射模型

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摘要

Automated analysis of ultrasonic images could be greatly improved with model-based Bayesian methods for image analysis. Such an approach would require an accurate probabilistic image model representing ultrasonic images in terms of the gross shape of underlying anatomical structure. Existing probabilistic models for ultrasonic image data do not adequately incorporate structure shape or system characteristics; thus, a substantially new approach is warranted. Toward that goal, we have developed models for the imaging system and rough surface with the following objectives: (1) accuracy in representation of basic image characteristics such as the texture and intensity, (2) a minimum of computational requirements, and (3) a form that is naturally extendable to an appropriate probabilistic image model. The imaging system was modeled as a linear system with a separable three-dimensional point-spread function with an envelope of Gaussian curves in each dimension. The rough surface was modeled as a collection of discrete scatterers placed on the continuum and parametrized by a surface roughness and scatterer concentration. Models were evaluated by a visual comparison of actual and simulated images of a cadaveric lumbar vertebra. The gross shape of the vertebral surface was estimated from computed tomography images of the vertebra, and simulated images were generated using the models and the gross surface shape. Actual images were registered with the surface and simulated images to within 2 mm. The similarity of the actual and simulated images was quite remarkable considering the simplicity of the models. Differences between the images were less than those between two simulated images separated by 0.4 mm or one-fifth the registration error. Further assessment of the models would require a statistical approach not yet available. The models do, however, provide the basis for the development of a computationally tractable probabilistic image model for image analysis. Such a model will provide the means for a statistical evaluation of the system and surface models.
机译：超声图像的自动分析可以通过基于模型的贝叶斯方法进行图像分析，从而大大提高。这样的方法将需要准确的概率图像模型，该图像模型根据基础解剖结构的总体形状来表示超声图像。现有的超声图像数据概率模型不能充分融合结构形状或系统特性；因此，需要一种全新的方法。为了实现这一目标，我们开发了具有以下目标的成像系统和粗糙表面模型：（1）准确表示基本图像特征（例如纹理和强度）；（2）最低计算要求；以及（3）一种自然可以扩展到适当概率图像模型的形式。成像系统被建模为具有可分离的三维点扩展函数的线性系统，每个维度都具有高斯曲线的包络。将粗糙表面建模为放置在连续体上的离散散射体的集合，并通过表面粗糙度和散射体浓度进行参数化。通过对尸体腰椎的实际图像和模拟图像进行视觉比较来评估模型。根据计算机的断层扫描图像估计椎骨表面的总体形状，并使用模型和总体表面形状生成模拟图像。将实际图像与表面对齐，并将模拟图像对齐到2毫米以内。考虑到模型的简单性，实际图像和模拟图像的相似性非常出色。图像之间的差异小于相隔0.4毫米或五分之一对位误差的两个模拟图像之间的差异。对模型的进一步评估将需要一种尚无统计方法。但是，这些模型确实为开发用于图像分析的可计算易处理的概率图像模型提供了基础。这样的模型将为系统和表面模型的统计评估提供手段。

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