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A Robust Level Set Framework For Medical Image Segmentation

机译:用于医学图像分割的鲁棒水平集框架

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摘要

In this paper, a new speed function of level set framework is presented. The region information, instead of the image gradient information, is fused into the level set fundamental model to improve the robustness of the segmentation for medical images. This new speed function is particularly well adapted to situations where edges are weak and overlap. A number of experiments on ultrasound (US), CT, MR and X-ray modalities medical images were performed to evaluate the new method. The experimental results show the proposed method is effective and robust.
机译:本文提出了一种新的水平集框架速度函数。将区域信息而不是图像梯度信息融合到级别集基本模型中,以提高医学图像分割的鲁棒性。这种新的速度功能特别适合于边缘较弱且重叠的情况。进行了许多关于超声(US),CT,MR和X射线模态医学图像的实验,以评估新方法。实验结果表明,该方法是有效且鲁棒的。

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