一种基于CT图像的肺癌病变检测方案

Jia Tong, Wei Ying, Wu Cheng Dong
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引用次数: 22

摘要

本文提出了一种新的肺结节计算机辅助检测方案。首先,采用自适应阈值等算法从CT数据中分割肺区域;其次,建立活动轮廓模型,在肺区准确分割和去除肺血管;接下来,检测可疑结节,并使用选择性形状过滤器过滤遗漏的肾血管;最后,提取结节特征,并使用基于规则的分类器区分真阳性和假阳性结节。实验结果表明,该方案可以帮助放射科医生提高诊断效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A lung cancer lesions dectection scheme based on CT image
A new computer-aided detection (CAD) scheme for detecting lung nodules is proposed in this paper. Firstly, the lung region is segmented from the CT data using adaptive threshold algorithm etc; Secondly, building active contour model to segment and remove lung vessel accurately in the lung region; Next, suspicious nodules are detected and omitted renal vessel is filtered using a selective shape filter; Finally, nodule features are extracted and rule-based classifier is used to distinguish true or false positive nodules. Experiment results indicate that this scheme can help radiologist improve the diagnosis efficiency.
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