基于灰度共生矩阵和梯度相位互信息的医学图像检索  

Medical Image Retrieval Based on Gray Level Co-occurrence Matrix and Gradient Phase Mutual Information

作　　者：支力佳[1,2] 张少敏[1,2] 赵大哲[1,2] 赵宏 

机构地区：[1]东北大学信息科学与工程学院,辽宁沈阳110004 [2]东北大学医学影像计算教育部重点实验室,辽宁沈阳110004

出　　处：《东北大学学报（自然科学版）》2010年第3期358-361,365,共5页Journal of Northeastern University(Natural Science)

基　　金：国家自然科学基金资助项目(60671050);辽宁省重大科技计划项目(2008402001);沈阳市重点技术创新计划项目(2008-9)

摘　　要：结合灰度共生矩阵特征和梯度相位互信息,提出了一种面向临床实际应用的两步匹配医学图像检索算法.该算法在提供良好分类性能的灰度共生矩阵特征的基础上,通过精化检索进一步提高了检索精度,以及检索算法的整体鲁棒性.使用该算法对包含有6种不同解剖部位的CT图像库进行检索实验.实验结果表明该算法在达到良好的检索准确性的同时,具有接近实时的查询响应速度.对该算法进行适当扩展,能容易地推广到实际医学检索应用中.A new two-step medical image retrieval algorithm for clinical practice was proposed combining the gray level co-occurrence matrix with gradient phase mutual information.Based on the good classification performance of gray level co-occurrence matrix,the algorithm refines the retrieved process to improve its precision and the integral robustness of the retrieval algorithm.With the algorithm applied to 6 different anatomical positions of CT images,the testing results showed that the algorithm can provide high precision while attaining near real-time speed of response to queries.Expanding properly the algorithm,it can be easily applied to clinical practice in wider fields.

关 键 词：基于内容图像检索 灰度共生矩阵 梯度相位匹配 梯度相位互信息 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]