Boukerroui Djamal, Noble J Alison, Brady Michael
HEUDIASYC, UMR CNRS #6599, Université de Technologie de Compiègne, BP 20529 - 60205 Compiègne Cedex, France.
Inf Process Med Imaging. 2003 Jul;18:586-98. doi: 10.1007/978-3-540-45087-0_49.
In this paper, we focus on velocity estimation in ultrasound images sequences. Ultrasound images present many difficulties in image processing because of the typically high level of noise found in them. Recently, Cohen and Dinstein have derived a new similarity measure, according to a simplified image formation model of ultrasound images, optimal in the maximum likelihood sense. This similarity measure is better for ultrasound images than others such as the sum-of-square differences or normalised cross-correlation because it takes into account the fact that the noise in an ultrasound image is multiplicative Rayleigh noise, and that displayed ultrasound images are log-compressed. In this work we investigate the use of this similarity measure in a block matching method. The underlying framework of the method is Singh's algorithm. New improvements are made both on the similarity measure and the Singh algorithm to provide better velocity estimates. A global optimisation scheme for algorithm parameter estimation is also proposed. We show that this optimisation makes an improvement of approximately 35% in comparison to the result obtained with the worst parameter set. Results on clinically acquired cardiac and breast ultrasound sequences, demonstrate the robustness of the method.
在本文中，我们专注于超声图像序列中的速度估计。由于超声图像中通常存在高水平的噪声，其在图像处理方面存在诸多困难。最近，科恩和丁斯坦根据超声图像的简化成像模型，推导出了一种新的相似性度量，该度量在最大似然意义上是最优的。这种相似性度量对于超声图像而言，比其他度量（如平方差之和或归一化互相关）更好，因为它考虑到了超声图像中的噪声是乘性瑞利噪声，以及显示的超声图像是对数压缩的这一事实。在这项工作中，我们研究了这种相似性度量在块匹配方法中的应用。该方法的基础框架是辛格算法。在相似性度量和辛格算法方面都进行了新的改进，以提供更好的速度估计。还提出了一种用于算法参数估计的全局优化方案。我们表明，与使用最差参数集获得的结果相比，这种优化使性能提高了约35%。对临床采集的心脏和乳腺超声序列的结果表明了该方法的稳健性。
