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Volume segmentation of susceptibility weighted images of the brain using a level set approach

机译:使用水平集方法对脑的敏感性加权图像进行体积分割

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An improved version of the variational level set algorithm was used for volume segmentation of susceptibility weighted images of the brain. Binary maps based on an intensity histogram were used as edge indicators in the level set evolution equation. The parameters of the evolution equation were optimized with a synthetic SWI phantom using a simulated annealing algorithm. The convergence condition for brain tissue segmentation was presented. The algorithm was applied to the mIP of the 3D magnitude images to define the brain contour, which was then used as the initialization contour in the volume segmentation in all slices. Robust volume segmentation was obtained and the signal loss in the peripheral regions of the brain was effectively reduced in the mIP display of the 3D SWI data. The level set algorithm provides a feasible solution for robust and fully automated volume segmentation for the display of 3D SWI of the brain.
机译:改进的变分水平集算法版本用于对大脑的磁化加权图像进行体积分割。基于强度直方图的二元图被用作水平集演化方程中的边缘指示符。使用模拟退火算法,使用合成的SWI幻像对演化方程的参数进行了优化。提出了脑组织分割的收敛条件。该算法已应用于3D幅值图像的mIP,以定义大脑轮廓,然后将其用作所有切片中体积分割的初始化轮廓。在3D SWI数据的mIP显示中,获得了稳健的体积分割,并有效减少了大脑外围区域的信号损失。水平集算法为大脑3D SWI的显示提供了鲁棒且全自动的体积分割的可行解决方案。

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