The traditional CT image segmentation algorithm is easy to ignore image contour initialization, which leads to the problem of long time consuming and low accuracy. A superpixel mesh CT image improved segmentation algorithm using active contour was proposed. CT image superpixel gridding was carried out first; secondly, on the basis of gridding, the region growth criterion was improved by superpixel processing, the region growth graph was established, the image edge salient graph was calculated based on the growth graph, and the target edge was obtained as the initial contour; finally, the Mumford-Shah model in the active contour model was improved; the energy functional was constructed based on the improved model and transformed into the symbol distance function. The results show that the proposed algorithm takes less time to mesh superpixels, the accuracy of image edge calculation is high, the correct classification coefficient is as high as 0.9, and the accuracy of CT image segmentation is always higher than 90%, which has superiority.

中文翻译：

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一种改进的基于主动轮廓的图像分割算法CT超像素网格

传统的CT图像分割算法容易忽略图像轮廓初始化，导致耗时长、准确率低的问题。提出了一种基于主动轮廓的超像素网格CT图像改进分割算法。首先进行CT图像超像素网格化；其次，在网格化的基础上，通过超像素处理改进区域生长准则，建立区域生长图，根据生长图计算图像边缘显着图，得到目标边缘作为初始轮廓；最后，改进了活动轮廓模型中的Mumford-Shah模型；基于改进模型构建能量泛函，转化为符号距离函数。结果表明，所提出的算法对超像素进行网格划分所需的时间更少，