计算机应用研究2017，Vol.34Issue(1)：290-294,5.DOI:10.3969/j.issn.1001-3695.2017.01.066
一种基于低剂量CT图像的肺结节分割方法
Method for pulmonary nodules segmentation based on low dose CT images
摘要
Abstract
In order to reduce the interference of noise and uneven distribution of gray level in low dose chest CT images while segmenting pulmonary nodules,this paper proposed a new segmentation method.The method incorporated a fuzzy speed func-tion into the active contour model,based on fuzzy membership degree,to be the evolution limit weight factor,which could e-liminate the interference of noise and other factors,as well as improve the evolution efficiency.Aiming at juxta-vascular nodules,the method fixed the membership degree by introducing a vessel characteristic coefficient to depress the influence of a plenty of vessel regions in the lung parenchyma.After experimental verification,the algorithm can improve the accuracy of the pulmonary nodules segmentation effectively and work better for juxta-vascular nodules.Besides,it also can reduce the error segmentation rate of obviously.关键词
低剂量CT/肺结节分割/活动轮廓模型/模糊速度函数/血管特征系数Key words
low dose CT/pulmonary nodule segmentation/active contour model/fuzzy speed function/vessel characteristic coefficient分类
信息技术与安全科学引用本文复制引用
黑啸吉,蒋慧琴,马岭,杨晓鹏,刘玉敏..一种基于低剂量CT图像的肺结节分割方法[J].计算机应用研究,2017,34(1):290-294,5.基金项目
国家自然科学基金资助项目（61271146）；郑州市科技创新团队资助项目（131PCXTD630）；2013年河南省科技型中小企业创新资金资助项目 （）
