区域医疗医学影像交互信息共享方法仿真
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摘要
医学影像交互信息共享是实现区域医疗信息化工程的一项关键内容,信息资源利用率和共享效率是实现医疗影像信息化建设应解决的主要问题。针对目前信息共享方法存在的可共享信息吞吐量低、效率低以及资源利用率不高等问题,提出一种新的区域医疗影像交互信息共享方法。根据影像交互信息加权向量的属性压缩结果得到语义指向性特征向量,对其进行量化编码;依据信息编码的唯一标识,结合数据清洗规则对特征向量进行封装处理,对封装处理后的数据进行质量评价,构建面向区域医疗影像交互信息的质量评价模型。对质量评分较高的信息进行统一整合,将整合后的交互信息传送给客户端,客户端对影像交互信息进行解压绘制,从解码后的备份文件中选取感兴趣的医疗医学影像区域进行优先显示,实现了交互式医疗医学影像信息共享。实验结果表明,所提方法有效提高了信息利用率、共享效率以及吞吐量。
For the low throughput of sharing information,low efficiency and low resource utilization,this article focuses on a new method of sharing regional medical image interactive information. Based on the attribute compression result of weight vector of image interaction information,we obtained the semantic directivity feature vector and quantized and encoded it. According to the unique identification of information coding,we used the data cleaning rule to encapsulate the feature vector. After that,we evaluated the quality of encapsulated data and constructed the quality evaluation model for regional medical image interaction information. In addition,we uniformly integrated the information with higher quality score,and then transmitted integrated interaction information to the client. Finally,the client decompressed and drew the image interaction information and selected the interested medical image region from the decoded backup file,which was used for priority display. Thus,we achieved the interactive medical image information sharing. Simulation results show that the proposed method effectively improves information utilization,sharing efficiency and throughput.
For the low throughput of sharing information,low efficiency and low resource utilization,this article focuses on a new method of sharing regional medical image interactive information. Based on the attribute compression result of weight vector of image interaction information,we obtained the semantic directivity feature vector and quantized and encoded it. According to the unique identification of information coding,we used the data cleaning rule to encapsulate the feature vector. After that,we evaluated the quality of encapsulated data and constructed the quality evaluation model for regional medical image interaction information. In addition,we uniformly integrated the information with higher quality score,and then transmitted integrated interaction information to the client. Finally,the client decompressed and drew the image interaction information and selected the interested medical image region from the decoded backup file,which was used for priority display. Thus,we achieved the interactive medical image information sharing. Simulation results show that the proposed method effectively improves information utilization,sharing efficiency and throughput.
引文
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[10] 杨臻.分布式多空间数据库信息快速融合方法仿真[J].计算机仿真,2017,34(6):427-430.
[2] 肖铭,陈平,何鸿谕,等.建设适应重庆地区的区域医学影像信息和大数据分析平台[J].中国生物工程杂志,2017,37(11):28-36.
[3] 周金海,印志鸿.区域医疗卫生信息及影像数字化资源的共享战略[J].中国组织工程研究,2016,13(28):9069-9073.
[4] 刘义,王霄英.大数据概念在医学影像中的应用探索[J].放射学实践,2016,31(12):1124-1126.
[5] 马灿.国内外医疗大数据资源共享比较研究[J].情报资料工作,2016,37(3):63-67.
[6] 梁涤青,陈志刚,邓小鸿.基于混沌加密的医学图像安全共享方案[J].小型微型计算机系统,2016,37(1):162-167.
[7] 张梦,翟圣云,苏栋骐.基于马赛克技术的秘密图像共享改进算法[J].计算机应用研究,2016,33(11):3480-3484..
[8] 杨进涛,沈寿林,黄斌.基于Multi-Agent的信息共享行为建模研究[J].火力与指挥控制,2017,42(9):121-124.
[9] 李永忠,董凌峰,吴真玮.基于系统动力学的政务信息共享博弈分析[J].电子科技大学学报(社科版),2017,19(1):35-42.
[10] 杨臻.分布式多空间数据库信息快速融合方法仿真[J].计算机仿真,2017,34(6):427-430.