海量医学数据处理框架及快速体绘制算法

设计并实现了一套针对海量数据的处理和分析算法框架,并将其融入实验室早先开发完成的医学影像算法研发平台MITK(medical imaging toolkit)中,真正建立起一个海量医学影像数据的处理平台,并在此基础上研究了针对海量数据的基于光线投射和三维纹理的快速体绘制算法,提出了一种半自适应分块的方法对原始数据进行分块,在不对分块速度产生太大影响的基础上得到了更好的分块结果,同时使用图形硬件来进一步加速整个算法的绘制流程.实验结果表明了该平台和算法对于海量医学数据处理和可视化的有效性.

Processing Framework and the Fast Volume Rendering Algorithms for Out- of-Core Medical Data

This paper designs and implements an algorithm framework for the out-of-core medical data processing and analyzing and integrates it into MITK (medical imaging toolkit), an algorithm toolkit for medical image processing and analyzing accomplished by the group. With the help of this, a processing platform for the out-of-core medical data is set up and fast out-of-core volume rendering algorithms based on volume ray casting and 3D texture are studied in this paper. A semi-adaptive partitioning method is proposed to divide original data sets into sub-blocks and get a better partitioning result without influencing the partitioning speed. Furthermore, the graphics hardware is also used to accelerate the rendering process. The experimental results indicate that the new framework and algorithms are effective and efficient for the processing and visualization of the out-of-core medical data sets.