基于Gabor小波变换的医学图像纹理特征分类

Gabor小波变换技术对医学CT图像进行纹理特征分类时，由于图像拍摄角度的变化会造成分类的误差。针对以上问题，在Gabor小波变换的基础上提出一种用于分析旋转不变医学图像的方法。该方法采用旋转规范化，即特征元素的循环移位使规范化后所有的图像都具有相同的主方向。实验结果表明，加入旋转规范化循环算子的Gabor小波变换在医学CT图像纹理特征分类时能够达到较好的精确度。

Medical Image Texture Features Classification Based on Gabor Wavelet Transform

In probabilistic database, aggregation query processes each possible world, but the number of possible may be exponentially growing with the increase of tuple number. So, aggregation query can not be calculated in linear time when there is a relatively large number of tuples. Three aggregation components are defined for each aggregation function. By encoding original probabilistic relation and using conversion, storage procedure and approximate calculation methods respectively, aggregation query can be implemented in linear time. Theoretical proof and experimental results show that the methodology used is correct and efficient.