【摘要】 针对多帧CT图像进行病理诊断中容易出现数据挖掘信息冗余导致误判的问题,提出一种基于CT图像序列关联轮廓线特征提取及批处理的多帧CT图像数据的测序数据挖掘方法。首先对采集的多帧CT图像进行去噪和特征优化处理,然后采用Sobel算子模板进行CT图像测序数据的模板匹配,基于边缘先验知识进行轮廓线套索搜索,并采用连续批处理方法进行多帧CT图像的连续测序数据挖掘与分析,提高处理效率。最后进行仿真分析,结果表明,采用该方法进行多帧CT图像处理,能准确提取图像轮廓线特征,实现测序数据的准确挖掘,且运算效率较高。更多还原

【Abstract】 Because the pathological diagnosis with multi-frame CT image may result in the erroneous judgment due to the redundant data mining information,a sequencing data mining method of multi-frame CT image data is put forward,which is based on the extraction and batch processing of the association contour line feature of CT image. The collected multi-frame CT image is performed with denoising and feature optimization. The Sobel operator template is adopted to match the template of the CT image sequencing data. The contour line is searched on the basis of edge prior knowledge. The continuous batch processing method is employed to mine and analyze the continuous sequencing data of the multi-frame CT image,and improve the processing efficiency. The simulation analysis results show that the method can extract the feature of image contour line accurately for multi-frame CT image processing,mine the sequencing data exactly,and has high operation efficiency.更多还原