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Segmentation of ultrasound images―multiresolution 2D and 3D algorithm based on global and local statistics

机译：超声图像分割-基于全局和局部统计的多分辨率2D和3D算法

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摘要

In this paper, we propose a robust adaptive region segmentation algorithm for noisy images, within a Bayesian framework. A multiresolution implementation of the algorithm is performed using a wavelets basis and can be used to process both 2D and 3D data. In this work we focus on the adaptive character of the algorithm and we discuss how global and local statistics can be utilised in the segmentation process. We propose an improvement on the adaptivity by introducing an enhancement to control the adaptive properties of the segmentation process. This takes the form of a weighting function accounting for both local and global statistics, and is introduced in the minimisation. A new formulation of the segmentation problem allows us to control the effective contribution of each statistical component. The segmentation algorithm is demonstrated on synthetic data, 2D breast ultrasound data and on echocardiographic sequences (2D + T). An evaluation of the performance of the proposed algorithm is also presented.
机译：在本文中，我们提出了一种在贝叶斯框架内针对噪声图像的鲁棒自适应区域分割算法。该算法的多分辨率实现是基于小波执行的，可用于处理2D和3D数据。在这项工作中，我们专注于算法的自适应特性，并讨论了如何在分割过程中利用全局和局部统计数据。我们通过引入增强功能来控制分割过程的自适应属性，提出对自适应性的改进。这采用权重函数的形式来说明本地和全局统计，并在最小化中引入。细分问题的新表述使我们能够控制每个统计成分的有效贡献。在合成数据，2D乳房超声数据和超声心动图序列（2D + T）上演示了分割算法。还对所提出算法的性能进行了评估。

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