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Soft-tissues Image Processing: Comparison of Traditional Segmentation Methods with 2D active Contour Methods

机译：软组织图像处理：传统分割方法与2D主动轮廓方法的比较

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Soft-tissues Image Processing: Comparison of Traditional Segmentation Methods with 2D active Contour MethodsThe paper deals with modern methods of image processing, especially image segmentation, classification and evaluation of parameters. It focuses primarily on processing medical images of soft tissues obtained by magnetic resonance tomography (MR). It is easy to describe edges of the sought objects using segmented images. The edges found can be useful for further processing of monitored object such as calculating the perimeter, surface and volume evaluation or even three-dimensional shape reconstruction. The proposed solutions can be used for the classification of healthy/unhealthy tissues in MR or other imaging. Application examples of the proposed segmentation methods are shown. Research in the area of image segmentation focuses on methods based on solving partial differential equations. This is a modern method for image processing, often called the active contour method. It is of great advantage in the segmentation of real images degraded by noise with fuzzy edges and transitions between objects. In the paper, results of the segmentation of medical images by the active contour method are compared with results of the segmentation by other existing methods. Experimental applications which demonstrate the very good properties of the active contour method are given.
机译：软组织图像处理：传统分割方法与2D主动轮廓方法的比较本文探讨了现代图像处理方法，尤其是图像分割，参数分类和评估。它主要专注于处理通过磁共振断层扫描（MR）获得的软组织的医学图像。使用分割图像很容易描述寻找对象的边缘。找到的边缘可用于进一步处理受监视对象，例如计算周长，评估表面和体积，甚至进行三维形状重建。提出的解决方案可用于MR或其他成像中健康/不健康组织的分类。显示了所提出的分割方法的应用示例。图像分割领域的研究集中在基于求解偏微分方程的方法上。这是一种现代的图像处理方法，通常称为主动轮廓法。它在分割由于模糊边缘和对象之间的过渡而被噪声降解的真实图像时具有很大的优势。在本文中，将主动轮廓法对医学图像的分割结果与其他现有方法对医学图像的分割结果进行了比较。给出了证明主动轮廓法非常好的特性的实验应用。

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