论文标题
基于定向的无环图和伪影模型的强度和结构的超声置信图
Ultrasound Confidence Maps of Intensity and Structure Based on Directed Acyclic Graphs and Artifact Models
论文作者
论文摘要
超声成像一直在改善，但是继续遭受固有的人工制品，这些工件对模型有挑战性，例如衰减，阴影，衍射，斑点等。这些伪像可能会混淆图像分析算法，除非尝试评估单个像素值的确定性。我们的新型置信算法使用基于超声成像的声学物理特性的定向无环图分析像素值。我们展示了我们的方法的独特功能，并将其与以前的置信度计算算法进行比较，用于阴影检测和图像复合任务。
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.
