海量数据的曲面分层重建算法

A Layered Surfaces Reconstruction Algorithm from Large Volumetric Datasets

从二维图像序列进行表面重建的问题由来已久,传统的重建方法通常是先重建或先等值面抽取,再简化数据量。随着处理数据量的增长,传统算法的中间过程会因为存储空间的限制不能进行下去,如何利用有限的存储空间对大数据量进行处理,从而完成曲面的重建曾是要研究的问题。针对大数据量的已分割的医学切片图像,利用逐层重建、即时简化的基本思想,给出一个易于操作实现、数据量可控制的算法。这样可以在硬件条件不太高的计算机(如内存不太大的个人微机)上实现大数据量的医学图像表面重建。

Surfaces reconstruction from serial section images is a mature research in visualization of medical imaging. Traditional methods always adopt the process that starts on mesh reconstruction or isosurfaces extraction, then data simplification. Since the volumetric datasets are huge and segmented. Most of the existing algorithms that use large in-core data structures might be too large to off-load to disk. In this paper, an algorithm is presented that has been designed to reconstruct 3D surfaces from huge and segmented volumetric datasets. Approximate mesh can be reconstructed and simplified layer by layer. And user can control mesh complexity by parameters. Because memories are used rationally, some surfaces reconstruction from large datasets can work on PC using the algorithm.