Segmentation of Ultrasound Images

描述(申请人提供)：分割是在图像中寻找边界的任务，是量化超声图像中的信息的重要阶段。这项拟议的研究旨在建立一个分割人类和动物心脏超声图像的计算框架。它基于两个思想：第一个思想是对超声信号使用概率模型，并将分割作为MAP估计问题。第二种算法使用一种称为隧道下降的新优化策略来计算MAP估计值。隧道下降算法具有逃脱MAP对数似然函数局部极大值的能力。初步结果将隧道下降结果与人工分割结果进行了比较，结果清楚地表明隧道下降在分割短轴超声图像方面优于经典的活动轮廓。实验评估还表明，该算法在初始化方面具有较强的健壮性，并且无需调整就能可靠工作。这项研究试图将这些想法扩展到分割更复杂的边界、具有镜面反射的边界、具有数据丢失的边界、超声图像序列中的移动边界、三维超声图像和R.F.超声波图像。这些延伸部分将用于在短轴和心尖四腔切面上联合分割Endo和Ei-Cardium。人体和实验动物的图像将被分割。这些图像将由联合调查员提供。将建立两个心脏超声体模，用于评估分割的准确性以及基于分割的体积和厚度计算的准确性。来自模型的射频数据也将被收集，并在软件中进行系统处理，以创建B模式图像。R.F.的分段。将图像与B超图像的分割进行比较，以了解机器处理对分割的影响。将人体和动物图像分割与人工分割进行比较。隧道下降的性能也将与模拟退火法进行比较。

DESCRIPTION (provided by applicant): Segmentation, which is the task of finding boundaries in an image, is an important stage in quantifying information in ultrasound images. The proposed research seeks to build a computational framework for segmenting human and animal cardiac ultrasound images. It is based on two ideas: the first uses a probabilistic model for the ultrasound signal and poses segmentation as a MAP estimation problem. The second uses a new optimization strategy called tunneling descent to calculate the MAP estimate. Tunneling descent has the capacity to escape from local maxima of the MAP log-likelihood function. Preliminary results, which compare tunneling descent results to manual segmentation, clearly show that tunneling descent outperforms classical active contours in segmenting short-axis ultrasound images. Experimental evaluation also shows that it is robust with respect to initialization and works reliably without tweaking. This research seeks to extend these ideas to segment more complex boundaries, boundaries with specularities, boundaries with data dropout, moving boundaries in ultrasound image sequences, 3-D ultrasound images, and r.f. ultrasound images. These extensions will be used to jointly segment the endo and epi-cardium in short axis and apical four-chamber views. Human as well as laboratory animal images will be segmented. These images will be made available by the co-investigators. Two cardiac ultrasound phantoms will be built and used for evaluating the accuracy of segmentation as well as accuracy of volume and thickening calculations that are based on segmentation. R.F. data from the phantom will also be collected and systematically manipulated in software to create B-mode images. The segmentation of the r.f. images will be compared to the segmentation of the B-mode images to understand the effect of machine processing on segmentation. The human and animal image segmentations will be compared to manual segmentation. The performance of tunneling descent will also be compared to simulated annealing.