基于粗糙模糊集理论的医学体数据场模糊增强算法

提出了一种基于粗糙模糊集理论的医学体数据场模糊增强算法.该算法首先应用粗糙集理论对医学体数据场进行分类,在分类的同时,去除了噪声.然后针对不同的子数据场采用不同的方法进行处理,其中对于体数据场的前景部分采用迭代的模糊集理论方法进行增强.实验结果表明此算法不仅能够有效提高体数据场中感兴趣物体的信息显示度,而且在去除体数场中噪声的同时,也避免了感兴趣物体在绘制显示时背景的"遮挡"现象.

A Fuzzy Enhancement Algorithm Based on Rough Fuzzy Sets Theory for the Medical Volumetric Data

An algorithm based on rough fuzzy sets theory is presented in this paper. In this algorithm, the volumetric data is classified using the rough sets theory, and the noise is removed simultaneously, then the classified sub-volumetric data is enhanced in different ways. Hereinto, the foreground of volumetric data is enhanced by the iterative fuzzy sets methods. The experimental results show that the presented algorithm not only improves the display degree for information of volume of interest, but also removes noise of the volumetric data. In addition, the algorithm can also effectively avoid the cover of background when the interested tissues and organs are displayed.