基于自适应体窗结构分析的肺结节检测方法  

Structural analysis based on adaptive window for pulmonary nodule detection

作　　者：王凯[1] 张煜[1] 刘哲星[1] 林炳权[2] 吴志强[1] 曹蕾[1] 

机构地区：[1]南方医科大学生物医学工程学院,广东广州510515 [2]南方医科大学南方医院影像中心,广东广州510515

出　　处：《南方医科大学学报》2014年第6期759-765,共7页Journal of Southern Medical University

基　　金：国家自然科学基金青年基金(61102114);国家自然科学基金(31271067);广东省教育部产学研项目(2011B090400037)~~

摘　　要：基于三维Hessian矩阵的肺结节检测方法具有很高的敏感性,却很难避免血管交叉区域产生假阳性。本文提出了一种基于自适应体窗结构分析的方法,首先利用体素的Hessian矩阵特征值设计结构系数分析其灰度分布特征;然后根据结构系数构建三维自适应体窗分析组织的局部结构特征;最后使用判别函数检测出结节。通过对17套真实肺部CT图像序列进行实验,结果表明本方法可以检测出不同大小和类型的30个结节,并有效减少了血管交叉区域产生的假阳性。结合自适应体窗的Hessian矩阵检测方法可以提高检测效率,减轻医生工作量,为肺结节后续的分割和治疗提供有力的支持。Radiographic detection of pulmonary nodules based on three-dimensional Hessian matrix is highly sensitive but frequently produces false positive results in areas where blood vessels intersect. We propose a novel approach to pulmonary nodule detection using Hessian matrix-based adaptive window structure analysis, in which the structure coefficients is used to differentiate a voxel that belongs to a nodule or vascular structures, followed by construction of the 3D adaptive window to analyze the local structure characteristics;the nodules were then detected using the discrimination function. The experimental results on pulmonary CT images from 17 patients showed a 100%detection sensitivity for nodules of varying sizes and types, with also significantly reduced false positive results generated by the vessel junctions. This approach provides valuable assistance to follow-up positioning and segmentation of the pulmonary nodules.

关 键 词：肺结节检测 HESSIAN矩阵 结构系数 自适应体窗 结构分析 

分 类 号：R563[医药卫生—呼吸系统] TP391.41[医药卫生—内科学]