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首页> 外文期刊>Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice >Segmentation of kidney from ultrasound B-mode images with texture-based classification.
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Segmentation of kidney from ultrasound B-mode images with texture-based classification.

机译：使用基于纹理的分类从超声B型图像中分割肾脏。

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摘要

The segmentation of anatomical structures from sonograms can help physicians evaluate organ morphology and realize quantitative measurement. It is an important but difficult issue in medical image analysis. In this paper, we propose a new method based on Laws' microtexture energies and maximum a posteriori (MAP) estimation to construct a probabilistic deformable model for kidney segmentation. First, using texture image features and MAP estimation, we classify each image pixel as inside or outside the boundary. Then, we design a deformable model to locate the actual boundary and maintain the smooth nature of the organ. Using gradient information subject to a smoothness constraint, the optimal contour is obtained by the dynamic programming technique. Experiments on different datasets are described. We find this method to be an effective approach.
机译：从超声图上解剖结构的分割可以帮助医生评估器官形态并实现定量测量。这是医学图像分析中一个重要但困难的问题。在本文中，我们提出了一种基于Laws微纹理能量和最大后验（MAP）估计的新方法，以构建概率可变形的肾脏分割模型。首先，使用纹理图像特征和MAP估计，将每个图像像素分类为边界内或边界外。然后，我们设计一个可变形的模型来定位实际边界并保持器官的平滑性质。使用受平滑度约束的梯度信息，可通过动态编程技术获得最佳轮廓。描述了不同数据集上的实验。我们发现这种方法是一种有效的方法。

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