基于Piella框架和DT-CWT的肺癌PET/CT自适应融合算法  

Self-adaption fusion algorithm for lung cancer PET/CT based on Piella frame and DT-CWT

作　　者：周涛[1] 陆惠玲[1] 魏兴瑜 夏勇[2] 

机构地区：[1]宁夏医科大学理学院,宁夏银川750004 [2]西北工业大学计算机学院,陕西西安710100

出　　处：《中国科学技术大学学报》2017年第1期10-17,共8页JUSTC

基　　金：国家自然科学基金(81160183;61561040);宁夏自然科学基金(NZ14085);陕西省语音与图像信息处理重点实验室开放课题(SJ2013003)资助

摘　　要：通过分析Piella框架和多尺度分析的理论,在Piella框架的基础上给出了四种像素级融合规则的构造方法,即四种融合路径,在第一种融合路径的基础上,提出了基于Piella框架和DTCWT的肺癌PET/CT自适应融合算法,该算法首先对已配准的PET和CT图像进行DTCWT变换;然后,根据低频子带的特点,考虑到病灶部位在整幅图像中所占的面积较小,合理处理医学图像的背景以凸现病灶,采用自适应组合隶属度函数的融合规则;其次而对高频子带系数的选取,根据高频子带反映图像的细节特性和边缘信息;再次,由于高频系数的选择对图像的清晰度、边缘失真程度影响大,故对高频分量的融合选择分解系数的能量差异作为匹配测度、区域能量作为活性测度,并将加权与选择的方法相结合确定决策因子对高频分量进行融合.最后进行了仿真实验,与其他像素级融合算法进行了比较,并对图像融合效果作客观评价,实验结果表明,该算法可以更好地凸现图像中病灶的边缘和纹理信息.By analyzing the Piella framework and multi-scale analysis theory, four methods, or fusion paths, for constructing pixel level fusion rules are presented on the basis of the Piella framework. A selfadaption fusion algorithm of PET/CT based on Piella frame and DT-CWT was proposed on the basis of the first fusion path, Firstly, DTCWT was used to decompose the registration PET and CT image to get the low-frequency and high-frequency components. Secondly, according to the characteristics of low-frequency, fully considering the area of lesions position was smaller in the whole image and the vital importance to highlight the lesions by dealing with the background of medical image reasonably, the low- frequency components are fused by self-adaption combination of membership function. Thirdly, according to the characteristics of high-frequency sub-bands which reflected details of images and edge information, and their great influences on the degree of image sharpness and edge distortion, the energy difference of decomposition coefficient was used as the matching measure, regional energy was used as an activity measure, and the combination of weighting and selection method was used to determine decision factor in high frequency component. Finally, two experiments were done, one a comparison with the other pixellevel fusion algorithms and the other an objective evaluation of fusion effect. The experimental results shown that the algorithm can better retain and show the edge and texture information of lesions.

关 键 词：Piella框架 双树复小波 PET/CT 医学图像融合 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]