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Modified fast marching and level set method for medical image segmentation

机译:改进的医学图像分割快速行进和水平集方法

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In this paper, an interactive segmentation method that combines fast marching and level set method is proposed. Level set segmentation involves solving the energy-based active contour minimizatio problems by the computation of geodesics or minimal distance curves. First, by selecting the seed point, the fast marching method is used to extract rough boundaries of the interested object. We modified the traditional fast marching method to capture the weak edges by introducing watershed transform. Then, the contour obtained from the fast marching method mentioned above is regarded as an initialization and the level set method is used to finely tune the contour. The algorithm is demonstrated on some medical images: segmentation of knee tissues in CT image and segmentation of brain tissues in MR image. The results show that this method can remove the small regions obtained from fast marching method and converge the desired boundary.
机译:本文提出了一种结合快速行进和水平集方法的交互式分割方法。水平集分割涉及通过测量测地线或最小距离曲线来解决基于能量的主动轮廓最小化问题。首先,通过选择种子点,使用快速行进方法提取感兴趣对象的粗略边界。通过引入分水岭变换,我们修改了传统的快速行进方法来捕获弱边缘。然后,将从上述快速行进方法获得的轮廓视为初始化,并使用水平设置方法对轮廓进行微调。该算法在一些医学图像上得到了证明:CT图像中的膝盖组织分割和MR图像中的脑组织分割。结果表明,该方法可以去除快速行进方法获得的小区域,并收敛所需的边界。

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