Ultrasound imaging has been improving, but continues to suffer from inherent artifacts that are challenging to model, such as attenuation, shadowing, diffraction, speckle, etc. These artifacts can potentially confuse image analysis algorithms unless an attempt is made to assess the certainty of individual pixel values. Our novel confidence algorithms analyze pixel values using a directed acyclic graph based on acoustic physical properties of ultrasound imaging. We demonstrate unique capabilities of our approach and compare it against previous confidence-measurement algorithms for shadow-detection and image-compounding tasks.

翻译：超声成像在不断改进,但继续受到难以建模的固有文物的困扰,如衰减、影子、折射、分解、斑点等等。这些文物可能会混淆图像分析算法,除非试图评估单个像素值的确定性。我们的新的信任算法利用以超声成像的声学物理特性为基础的定向像素图分析像素值。我们展示了我们的方法的独特能力,并将其与先前的影子探测和图像合成任务的信任算法进行比较。