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A fast algorithm for medical image segmentation based on improved incremental variational level set

机译:基于改进增量变分水平集的医学图像快速分割算法

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According to the low calculating speed of Chan-Vese model for image segmentation caused by the iteration in process of evolution in the whole image region, a fast medical image segmentation method based on improved incremental variational level set is presented in this paper, in which incremental mode is adopted to get average gray value in iteration and a progressive iterative formula is used as the modification of analytical formula, so that some fast algorithms such as narrowband method could be applied to increase the efficiency of segmentation which makes the model more practical.
机译:针对Chan-Vese模型在整个图像区域演化过程中的迭代迭代导致的图像分割速度慢的问题,提出了一种基于改进的增量变分水平集的医学快速图像分割方法,其中采用迭代方式求平均灰度值,并用渐进式迭代公式进行解析公式的修正,可以应用窄带法等快速算法提高分割效率,使模型更加实用。

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