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Segmentation of 3-D medical image data sets with a combination of region based initial segmentation and active surfaces

机译:结合基于区域的初始分割和活动表面对3-D医学图像数据集进行分割

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摘要

Segmentation is an essential step in the analysis of medical images. For segmentation of 3-D data sets in clinical practice segmentation methods are necessary which have a small user interaction time and which are highly flexible. For this purpose we propose a two-step segmentation approach. The first step results in a coarse segmentation using the Image Foresting Transformation. In the second step an active surface creates the final segmentation. Our segmentation method was tested for segmentation on real CT images. The performance was compared with the manual segmentation. We found our method to work reliable.
机译:分割是医学图像分析中必不可少的步骤。为了在临床实践中对3D数据集进行分割,必须使用分割方法,这些方法具有较短的用户交互时间并且具有高度的灵活性。为此,我们提出了两步细分方法。第一步是使用图像森林变换进行粗分割。在第二步中,活动表面创建最终的分割。我们的分割方法经过测试,可以在真实的CT图像上进行分割。将效果与手动细分进行了比较。我们发现我们的方法工作可靠。

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