Shankar P Mohana
Department of Electrical & Computer Engineering, Drexel University, Philadelphia, Pennsylvania, USA.
Ultrasound Med Biol. 2015 Jan;41(1):268-80. doi: 10.1016/j.ultrasmedbio.2014.08.006. Epub 2014 Oct 22.
The existence of edges and boundaries in regions of interest (ROIs) in B-scan images alters the statistics of the backscattered echo from the ROI. Boundaries are the result of at least two different types of scattering scenarios in tissue, and the Nakagami model, which is being used extensively in ultrasound, is unlikely to fit the statistics of the backscattered echo under these conditions. Furthermore, there are very few other statistical models exist that describe the statistics of the backscattered echo from regions containing boundaries. In this work, the gamma mixture density and the recently proposed McKay density are explored as two viable models to fill this void. Justifications of these models are presented along with methods for estimating their parameters. Random number simulations and studies on tissue-mimicking phantoms indicate that the McKay and gamma mixture densities are the best for the modeling of the backscattered echo intensity when boundaries are present in the regions of interest.
B 超图像中感兴趣区域(ROI)内边缘和边界的存在改变了该 ROI 后向散射回波的统计特性。边界是组织中至少两种不同类型散射情况的结果,而在超声领域广泛使用的 Nakagami 模型在这些条件下不太可能拟合后向散射回波的统计特性。此外,几乎没有其他统计模型能够描述包含边界区域的后向散射回波的统计特性。在这项工作中,探索了伽马混合密度和最近提出的麦凯密度作为填补这一空白的两种可行模型。文中给出了这些模型的合理性说明以及估计其参数的方法。随机数模拟和对组织模拟体模的研究表明,当感兴趣区域存在边界时,麦凯密度和伽马混合密度最适合用于对后向散射回波强度进行建模。