目前针对体外震波碎石(ESWL)系统的研究主要集中在能量发射的研究,而对信息的优化采集研究较少.为了达到既碎裂结石又不损害人体组织的目的,如何高效地采集碎石影像数据成为一个重要的研究课题.提出的策略在医疗活动信息化的基础上,根据影像像素分布情况,利用自适应选择子,自动选择不同小波所对应的提升方案对数据进行分解,在有效剔除噪声的同时保持了人体组织的关键特征.为了最小化降噪后的均方误差,采用双元软阈值进行滤波.该研究为临床工作者提供更为准确的、智能化的数据和临床决策支持能力.实验结果验证了该提升方案的低时间复杂度和接近于传统小波的极小的重构误差,在定量分析和临床观测方面都获得了更好的结果. At present, the research on extracorporeal shock wave lithotripsy (ESWL) system mainly focuses on the energy emission research, but less on the information acquisition optimization.In order to achieve both the purpose of both broken stones and human tissue damage, how to efficiently collect gravel The image data has become an important research topic.On the basis of informationization of medical activities, the proposed strategy uses the adaptive selector to automatically select the lifting scheme corresponding to different wavelet to decompose the data according to the pixel distribution of the image, Noise while maintaining the key features of human tissue.In order to minimize the mean square error after noise reduction, the use of binary soft threshold filtering.The study provides clinicians with more accurate and intelligent data and clinical decision support Ability.The experimental results verify the low time complexity of the lifting scheme and the minimum reconstruction error close to the traditional wavelet, and obtain better results both in quantitative analysis and clinical observation.