基于3维图像增强的肺结节识别  

Recognition of pulmonary nodules based on 3D image enhancement

作　　者：刘阳[1,2] 赵大哲[1] 刘积仁[2] 

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

出　　处：《中国图象图形学报》2011年第8期1402-1407,共6页Journal of Image and Graphics

基　　金：国家自然科学基金项目(60671050;61001047)

摘　　要：孤立性肺结节的检测是肺癌早期诊断的关键。针对传统点增强滤波器虽然对结节增强具有很好的敏感性,但是却产生很多假阳性区域的问题,提出一种通过计算3维增强密度指数和判别规则来识别肺结节的方法。首先采用自适应双边滤波器对CT图像序列进行降噪和平滑处理;然后计算对应的Hessian矩阵及其特征值得到预增强系数,并获得感兴趣体区域,通过对预增强系数的分析来构造3维增强密度指数;最后应用判别规则对感兴趣体进行识别。针对两个肺部CT图像数据集对该方法进行了测试,结果表明,在识别孤立性肺结节方面该方法是有效的。The detection of solitary pulmonary nodules （SPN） is proven to be of critical importance in early-stage lung cancer diagnosis. Aiming at reducing the false positive regions caused by the dot enhancement filter which is sensitive to the lung nodules, a new recognition method based on calculation of three-dimensional （3D） enhancement density index and decision rule is proposed. An adaptive bilateral filter is applied to reduce the noisy and smooth CT image sequences. Then, the pre-enhancement coefficients and volume of interest （VOI） are obtained by computing the Hessian matrix and corresponding eigenvalues. After analyzing the distribution of pre-enhaneement coefficients, 3D enhancement density index is constructed. Finally, a decision rule is adopted to identify nodule candidates. The proposed method is tested on two lung CT image sets. The experimental results illustrate the efficiency of the proposed algorithm.

关 键 词：点增强 自适应双边滤波 HESSIAN矩阵 增强密度指数 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]