Wall position and thickness estimation from sequences of echocardiographic images
Wall position and thickness estimation from sequences of echocardiographic images
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DOI：
10.1109/42.481438
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发表时间：
1996-02-01
影响因子：
10.6
通讯作者：
Leitao, JMN
中科院分区：
文献类型：
--
作者：
Dias, JMB;Leitao, JMN
本文提出了一种从超声心动图图像序列中估计心内膜（内）和心外膜（外）轮廓的新方法。本文介绍的框架是在乳头肌水平上针对胸骨旁短轴视图进行微调的，底层模型是概率性的;它捕获了图像生成物理机制和心脏形态的相关特征。轮廓序列被假定为二维非因果一阶马尔可夫随机过程，每个变量都有一个空间索引和时间索引，图像像素被建模为瑞利分布的随机变量，其平均值取决于它们的位置（内膜内，内膜和心包之间，或心包外）。在贝叶斯框架下建立了完整的概率模型。本文采用最大后验概率（MAP）作为估计准则，引入了迭代多重网格动态规划（IMDP）算法，解决了轮廓和分布参数的联合估计这一优化问题。这是一个完全数据驱动的方案，没有特别的参数。该方法是在一个普通的工作站上实现的，导致计算时间与业务使用兼容，模拟和真实的图像的实验。
This paper presents a new method for endocardial (inner) and epicardial (outer) contour estimation from sequences of echocardiographic images, The framework herein introduced is fine-tuned for parasternal short axis views at the papillary muscle level, The underlying model is probabilistic; it captures the relevant features of the image generation physical mechanisms and of the heart morphology. Contour sequences are assumed to be two-dimensional noncausal first-order Markov random processes; each variable has a spatial index and a temporal index, The image pixels are modeled as Rayleigh distributed random variables with means depending on their positions (inside endocardium, between endocardium and pericardium, or outside pericardium). The complete probabilistic model is built under the Bayesian framework. As estimation criterion the maximum a posteriori (MAP) is adopted, To solve the optimization problem, one is led to (joint estimation of contours and distributions' parameters), we introduce an algorithm herein named iterative multigrid dynamic programming (IMDP). It is a fully data-driven scheme with no ad-hoc parameters. The method is implemented on an ordinary workstation, leading to computation times compatible with operational use, Experiments with simulated and real images are presented.