基于在线字典学习的医学图像特征提取与融合  

Medical Image Features Extraction and Fusion Based on Online Dictionary Learning

作　　者：吴双[1] 邱天爽[1] 高珊[1] 

机构地区：[1]大连理工大学电子信息与电气工程学部,大连116024

出　　处：《中国生物医学工程学报》2014年第3期283-288,共6页Chinese Journal of Biomedical Engineering

基　　金：国家自然科学基金(81241059;61172108);国家科技支撑计划项目(2012BAJ18B06)

摘　　要：提出一种基于在线字典学习(ODL)的医学图像特征提取与融合的新算法。首先,采用大小为8像素×8像素的滑动窗处理源图像,得到联合矩阵;通过ODL算法得到该联合矩阵的冗余字典,并利用最小角回归算法(LARS)计算该联合矩阵的稀疏编码;将稀疏编码列向量的1范数作为稀疏编码的活动级测量准则,然后根据活动级最大准则融合稀疏编码;最后根据融合后的稀疏编码和冗余字典重构融合图像。实验图像为20位患者的已配准脑部CT和MR图像,采用5种性能指标评价融合图像的质量,同两种流行的融合算法比较。结果显示,所提出算法的各项客观指标均值最优,Piella指数、QAB/F指数、MIAB/F指数、BSSIM指数和空间频率的均值分别为0.800 4、0.552 4、3.630 2、0.726 9和31.941 3,融合图像对比度、清晰度高,病灶的边缘清晰,运行速度较快,可以辅助医生诊断和临床治疗。An image features extraction and fusion algorithm based on online dictionary learning （ODL） is presented in this paper.Firstly,source images were combined into a joint matrix by the sliding window technique,the size of the sliding window was 8 × 8,the over-complete dictionary was trained by ODL algorithm and the sparse codes were acquired by LARS algorithm; the activity level measurement of sparse codes was the L1 norm of its vector,then,the sparse codes were fused by activity level maximum rule; finally,the fused image was reconstructed by over-complete dictionary and fused sparse codes.Co-aligned medical images of twenty patients were tested by experiments and the quality of the fused image was evaluated by five kinds of commonly used objective criterions.Compared with the other two popular medical image fusion algorithms,objective criterions of the fusion result show the advantage of the proposed algorithm,the mean of Piella,QAB/F,MIAB/F,BSSIM and space frequency index is 0.800 4,0.552 4,3.630 2,0.726 9 and 31.941 3,the fusion images of the proposed algorithm have high definition and contrast,clear texture and edge and fast speed,showing its application potentials of aiding clinical diagnoses and treatment.

关 键 词：图像融合 在线字典学习算法(ODL) 最小角回归算法(LARS) 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] TN911.73[自动化与计算机技术—计算机科学与技术]