Lung Nodules Detection in CT Images Using Gestalt-Based Algorithm  

作　　者：QIU Shi WEN Desheng CUI Ying FENG Jun 

机构地区：[1]Xi'an Institute of Optics and Precision Mechanics of CAS, Xi'an 710119, China [2]University of Chinese Academy of Sciences, Beijing 100059, China [3]College of Equipment Engineering, Engineering University of Chinese Armed Police Force, Xi'an 710086, China [4]School of Information Science and Technology, Northwest University, Xi'an 710127, China

出　　处：《Chinese Journal of Electronics》2016年第4期711-718,共8页电子学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.61372046);the Natural Science Foundation of Shannxi Province,China(No.2014JM8338)

摘　　要：To overcome low accuracy and high false positive of existing computer-aided lung nodules detection.We propose a novel lung nodule detection scheme based on the Gestalt visual cognition theory.The proposed scheme involves two parts which simulate human eyes cognition features such as simplicity,integrity and classification.Firstly,lung region was segmented from lung Computed tomography(CT) sequences.Then local threedimensional information was integrated into the Maximum intensity pro jection(MIP) images from axial,coronal and sagittal profiles.In this way,lung nodules and vascular are strengthened and discriminated based on pathologic image characteristics of lung nodules.The experimental database includes fifty-three high resolution CT images contained lung nodules,which had been confirmed by biopsy.The experimental results show that,the accuracy rate of the proposed algorithm achieves 91.29%.The proposed framework improves performance and computation speed for computer aided nodules detection.To overcome low accuracy and high false positive of existing computer-aided lung nodules detection.We propose a novel lung nodule detection scheme based on the Gestalt visual cognition theory.The proposed scheme involves two parts which simulate human eyes cognition features such as simplicity,integrity and classification.Firstly,lung region was segmented from lung Computed tomography（CT） sequences.Then local threedimensional information was integrated into the Maximum intensity pro jection（MIP） images from axial,coronal and sagittal profiles.In this way,lung nodules and vascular are strengthened and discriminated based on pathologic image characteristics of lung nodules.The experimental database includes fifty-three high resolution CT images contained lung nodules,which had been confirmed by biopsy.The experimental results show that,the accuracy rate of the proposed algorithm achieves 91.29%.The proposed framework improves performance and computation speed for computer aided nodules detection.

关 键 词：Computer-aided detection（CAD） Lung nodules Maximum intensity pro jection（MIP） 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP391.72[自动化与计算机技术—计算机科学与技术]