基于改进自生成神经网络的肺部CT序列图像分割

摘要

针对肺实质序列图像分割方法的时效性低和分割不完全性等问题,利用先验知识得到肺部CT序列ROI图像,提出超像素序列分割算法对ROI序列图像进行分割,采用改进的自生成神经网络对超像素进行聚类并优化,根据聚类后样本的灰度和位置特征识别肺实质区域.在序列肺实质图像的分割结果中,单张CT图像的平均处理时间为0.61s,同时能达到92.09±1.52％的平均肺部体素重合度.与已有的方法相比,所提算法能在相对较短的时间内获得较高的分割精准度.%Existing lung segmentation methods cannot fully segment all lung parenchyma images and have slow processing speed.The position of the lung was used to obtain lung ROI sequences,and an algorithm of superpixel sequences segmentation was then proposed to segment the ROI image sequences.In addition,improved self-generating neural networks were utilized for superpixel clustering and the grey and geometric features were extracted to identify and segment lung image sequences.The experimental results show that our method's average processing time is 0.61 second for a single slice and it can achieve average volume pixel overlap ratio of 92.09±1.52％.Compared with the existing methods,our method has higher segmentation precision and accuracy with less time.