Two-dimensional blind Bayesian deconvolution of medical ultrasound images
Two-dimensional blind Bayesian deconvolution of medical ultrasound images
复制标题
医学超声图像的二维盲贝叶斯反卷积
DOI：
--
复制
发表时间：
2008
期刊：
影响因子：
--
通讯作者：
T. Taxt
中科院分区：
文献类型：
--
作者：
R. Jiřík;T. Taxt
提出了一种基于贝叶斯框架的二维超声图像盲解卷积新方法。射频图像数据被建模为点扩散函数和组织函数的卷积，具有加性白色噪声。反卷积算法是从关于组织函数、点扩散函数和噪声的统计假设导出的。它被解决作为一个迭代优化问题。在每次迭代中，附加的约束被应用作为投影算子以进一步稳定该过程。所提出的方法是同态反褶积的扩展，这里只用于计算点扩散函数的初始估计。同态反卷积是基于点扩散函数和组织函数位于倒谱域的不同频带的假设，这是不完全正确的。在随后的迭代去卷积中放松该限制约束。去卷积被全局地应用于完整的射频图像数据。因此，仅考虑点扩散函数的全局部分。这种方法，加上只需要几次迭代，使反卷积潜在地用于实时应用。对体模和临床图像的测试表明，与输入图像和单独的同态去卷积的结果相比，去卷积给出了稳定的结果，具有明显更高的空间分辨率和更好的组织结构。
A new approach to 2-D blind deconvolution of ultrasonic images in a Bayesian framework is presented. The radio-frequency image data are modeled as a convolution of the point-spread function and the tissue function, with additive white noise. The deconvolution algorithm is derived from statistical assumptions about the tissue function, the point-spread function, and the noise. It is solved as an iterative optimization problem. In each iteration, additional constraints are applied as a projection operator to further stabilize the process. The proposed method is an extension of the homomorphic deconvolution, which is used here only to compute the initial estimate of the point-spread function. Homomorphic deconvolution is based on the assumption that the point-spread function and the tissue function lie in different bands of the cepstrum domain, which is not completely true. This limiting constraint is relaxed in the subsequent iterative deconvolution. The deconvolution is applied globally to the complete radiofrequency image data. Thus, only the global part of the point-spread function is considered. This approach, together with the need for only a few iterations, makes the deconvolution potentially useful for real-time applications. Tests on phantom and clinical images have shown that the deconvolution gives stable results of clearly higher spatial resolution and better defined tissue structures than in the input images and than the results of the homomorphic deconvolution alone.