A canonical form-based approach to affine registration of DTI

Due to the orientation feature of diffusion tensor images (DTI), tensors need to be reoriented during an affine registration. There exists two active reorientation schemes: finite strain (FS) and preserving principal direction (PPD). However, FS scheme limits its application on rigid deformation and PPD scheme suffers from computation load caused by the iteration. In order to overcome these shortcomings, we propose a canonical form-based affine registration of DTI, named as CFARD. We transform voxel sets into canonical forms where an affine registration is simplified as a rigid registration, while still preserves the effects of non-rigid components. This transforming thus extends the application of FS scheme to affine deformation. Furthermore, to reduce computation load, the quaternion technique is skillfully employed to seek a closed-form solution of the optimal rotation where no iteration is required. Extensive experiments are conducted on synthetic and real DTI data from the human brain. In contrast to four existing algorithms, the proposed CFARD improves the consistency between tensor orientation and the anatomical structures after deformation, and performs a better balance between accuracy and computational complexity.

     

面向健康云的定制化网络安全服务

针对传统的云计算网络安全模式无法满足云用户不同的网络安全需求,本文提出一种面向健康云的定制化网络安全服务方案.该方案首先根据健康云用户的网络安全需求填写安全模板;然后,将安全模板进行加密;最后,由定制化网络安全服务系统自动分析处理,建立有效的安全规则,最终保护云用户安全.在医疗健康云平台上展开实验,对比传统方法,实验结果显示本文方法在性能改善上的有效性.