• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

基于模糊综合主动轮廓模型和混合参数混合模型的 CT 图像肺结节检测。

Detection of pulmonary nodules in CT images based on fuzzy integrated active contour model and hybrid parametric mixture model.

机构信息

School of Automation Science and Engineering, South China University of Technology, Guangdong, Guangzhou 510640, China.

出版信息

Comput Math Methods Med. 2013;2013:515386. doi: 10.1155/2013/515386. Epub 2013 Apr 16.

DOI:10.1155/2013/515386
PMID:23690876
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3652289/
Abstract

The segmentation and detection of various types of nodules in a Computer-aided detection (CAD) system present various challenges, especially when (1) the nodule is connected to a vessel and they have very similar intensities; (2) the nodule with ground-glass opacity (GGO) characteristic possesses typical weak edges and intensity inhomogeneity, and hence it is difficult to define the boundaries. Traditional segmentation methods may cause problems of boundary leakage and "weak" local minima. This paper deals with the above mentioned problems. An improved detection method which combines a fuzzy integrated active contour model (FIACM)-based segmentation method, a segmentation refinement method based on Parametric Mixture Model (PMM) of juxta-vascular nodules, and a knowledge-based C-SVM (Cost-sensitive Support Vector Machines) classifier, is proposed for detecting various types of pulmonary nodules in computerized tomography (CT) images. Our approach has several novel aspects: (1) In the proposed FIACM model, edge and local region information is incorporated. The fuzzy energy is used as the motivation power for the evolution of the active contour. (2) A hybrid PMM Model of juxta-vascular nodules combining appearance and geometric information is constructed for segmentation refinement of juxta-vascular nodules. Experimental results of detection for pulmonary nodules show desirable performances of the proposed method.

摘要

计算机辅助检测 (CAD) 系统中各种类型结节的分割和检测存在各种挑战，特别是当 (1) 结节与血管相连且它们具有非常相似的强度时；(2) 具有磨玻璃密度 (GGO) 特征的结节具有典型的弱边缘和强度非均匀性，因此很难定义边界。传统的分割方法可能会导致边界泄漏和“弱”局部最小值的问题。本文针对上述问题进行了研究。提出了一种改进的检测方法，该方法结合了基于模糊综合活动轮廓模型 (FIACM) 的分割方法、基于毗邻血管结节的参数混合模型 (PMM) 的分割细化方法和基于知识的 C-SVM (代价敏感支持向量机) 分类器，用于在计算机断层扫描 (CT) 图像中检测各种类型的肺结节。我们的方法有几个新颖的方面：(1) 在提出的 FIACM 模型中，融合了边缘和局部区域信息。模糊能量被用作活动轮廓演化的驱动力。(2) 构建了一种结合外观和几何信息的毗邻血管结节混合 PMM 模型，用于毗邻血管结节的分割细化。肺结节检测的实验结果表明，该方法具有良好的性能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3837/3652289/51625d8f1426/CMMM2013-515386.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3837/3652289/66c7fce3d2ca/CMMM2013-515386.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3837/3652289/a9b943f4a91b/CMMM2013-515386.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3837/3652289/c26b45ec9462/CMMM2013-515386.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3837/3652289/1fbb532d0f51/CMMM2013-515386.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3837/3652289/5f1de553faee/CMMM2013-515386.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3837/3652289/ad0524cb7d86/CMMM2013-515386.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3837/3652289/b9f8f6750ffa/CMMM2013-515386.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3837/3652289/7a00415e4565/CMMM2013-515386.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3837/3652289/51625d8f1426/CMMM2013-515386.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3837/3652289/66c7fce3d2ca/CMMM2013-515386.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3837/3652289/a9b943f4a91b/CMMM2013-515386.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3837/3652289/c26b45ec9462/CMMM2013-515386.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3837/3652289/1fbb532d0f51/CMMM2013-515386.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3837/3652289/5f1de553faee/CMMM2013-515386.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3837/3652289/ad0524cb7d86/CMMM2013-515386.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3837/3652289/b9f8f6750ffa/CMMM2013-515386.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3837/3652289/7a00415e4565/CMMM2013-515386.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3837/3652289/51625d8f1426/CMMM2013-515386.009.jpg

相似文献

1
Detection of pulmonary nodules in CT images based on fuzzy integrated active contour model and hybrid parametric mixture model.基于模糊综合主动轮廓模型和混合参数混合模型的 CT 图像肺结节检测。
Comput Math Methods Med. 2013;2013:515386. doi: 10.1155/2013/515386. Epub 2013 Apr 16.
2
Segmentation of pulmonary nodules using adaptive local region energy with probability density function-based similarity distance and multi-features clustering.基于概率密度函数相似性距离和多特征聚类的自适应局部区域能量法用于肺结节分割
Biomed Eng Online. 2016 May 5;15(1):49. doi: 10.1186/s12938-016-0164-3.
3
Fuzzy speed function based active contour model for segmentation of pulmonary nodules.基于模糊速度函数的主动轮廓模型用于肺结节分割
Biomed Mater Eng. 2014;24(1):539-47. doi: 10.3233/BME-130840.
4
Juxta-Vascular Pulmonary Nodule Segmentation in PET-CT Imaging Based on an LBF Active Contour Model with Information Entropy and Joint Vector.基于具有信息熵和联合向量的LBF主动轮廓模型的PET-CT成像中的血管旁肺结节分割
Comput Math Methods Med. 2018 Jan 8;2018:2183847. doi: 10.1155/2018/2183847. eCollection 2018.
5
A Segmentation Framework of Pulmonary Nodules in Lung CT Images.肺部CT图像中肺结节的分割框架
J Digit Imaging. 2016 Feb;29(1):86-103. doi: 10.1007/s10278-015-9801-9.
6
Shape-based computer-aided detection of lung nodules in thoracic CT images.基于形状的胸部CT图像中肺结节的计算机辅助检测
IEEE Trans Biomed Eng. 2009 Jul;56(7):1810-20. doi: 10.1109/TBME.2009.2017027.
7
Effect of segmentation algorithms on the performance of computerized detection of lung nodules in CT.分割算法对CT中肺结节计算机检测性能的影响。
Med Phys. 2014 Sep;41(9):091906. doi: 10.1118/1.4892056.
8
3D skeletonization feature based computer-aided detection system for pulmonary nodules in CT datasets.基于 3D 骨架特征的 CT 数据集肺结节计算机辅助检测系统。
Comput Biol Med. 2018 Jan 1;92:64-72. doi: 10.1016/j.compbiomed.2017.11.008. Epub 2017 Nov 11.
9
Research on a pulmonary nodule segmentation method combining fast self-adaptive FCM and classification.一种结合快速自适应模糊C均值聚类算法与分类的肺结节分割方法的研究
Comput Math Methods Med. 2015;2015:185726. doi: 10.1155/2015/185726. Epub 2015 Apr 7.
10
Computerized detection of lung nodules in thin-section CT images by use of selective enhancement filters and an automated rule-based classifier.通过使用选择性增强滤波器和基于规则的自动分类器对薄层CT图像中的肺结节进行计算机化检测。
Acad Radiol. 2008 Feb;15(2):165-75. doi: 10.1016/j.acra.2007.09.018.

引用本文的文献

1
Automatic Liver Segmentation in CT Images with Enhanced GAN and Mask Region-Based CNN Architectures.基于增强型 GAN 和掩模区域的 CNN 架构的 CT 图像自动肝脏分割。
Biomed Res Int. 2021 Dec 16;2021:9956983. doi: 10.1155/2021/9956983. eCollection 2021.
2
Negative CT Contrast Agents for the Diagnosis of Malignant Osteosarcoma.用于恶性骨肉瘤诊断的阴性CT造影剂
Adv Sci (Weinh). 2019 Oct 11;6(23):1901214. doi: 10.1002/advs.201901214. eCollection 2019 Dec.
3
Segmentation of pulmonary nodules using adaptive local region energy with probability density function-based similarity distance and multi-features clustering.

本文引用的文献

1
Model-based learning using a mixture of mixtures of Gaussian and uniform distributions.基于模型的学习，使用混合高斯和均匀分布的混合物。
IEEE Trans Pattern Anal Mach Intell. 2012 Apr;34(4):814-7. doi: 10.1109/TPAMI.2011.199.
2
Automated segmentation refinement of small lung nodules in CT scans by local shape analysis.基于局部形状分析的 CT 扫描中小肺结节的自动分割细化。
IEEE Trans Biomed Eng. 2011 Dec;58(12):3418-28. doi: 10.1109/TBME.2011.2167621. Epub 2011 Sep 12.
3
Iterative narrowband-based graph cuts optimization for geodesic active contours with region forces (GACWRF).
基于概率密度函数相似性距离和多特征聚类的自适应局部区域能量法用于肺结节分割
Biomed Eng Online. 2016 May 5;15(1):49. doi: 10.1186/s12938-016-0164-3.
4
Classification of pulmonary nodules by using hybrid features.使用混合特征对肺结节进行分类。
Comput Math Methods Med. 2013;2013:148363. doi: 10.1155/2013/148363. Epub 2013 Jun 25.
基于迭代窄带的图割优化的带区域力的测地线主动轮廓模型（GACWRF）。
IEEE Trans Image Process. 2012 Jan;21(1):284-96. doi: 10.1109/TIP.2011.2160955. Epub 2011 Jun 30.
4
Pulmonary sarcoidosis: typical and atypical manifestations at high-resolution CT with pathologic correlation.肺部肉样瘤病：高分辨率 CT 的典型和非典型表现及与病理学的相关性。
Radiographics. 2010 Oct;30(6):1567-86. doi: 10.1148/rg.306105512.
5
Segmentation of pulmonary nodules of various densities with morphological approaches and convexity models.基于形态学方法和凸包模型的肺部结节的分割。
Med Image Anal. 2011 Feb;15(1):133-54. doi: 10.1016/j.media.2010.08.005. Epub 2010 Sep 21.
6
A new computationally efficient CAD system for pulmonary nodule detection in CT imagery.一种新的计算效率高的 CT 图像肺结节检测 CAD 系统。
Med Image Anal. 2010 Jun;14(3):390-406. doi: 10.1016/j.media.2010.02.004. Epub 2010 Feb 19.
7
Segmentation of interwoven 3d tubular tree structures utilizing shape priors and graph cuts.利用形状先验和图割对交织的 3D 管状树结构进行分割。
Med Image Anal. 2010 Apr;14(2):172-84. doi: 10.1016/j.media.2009.11.003. Epub 2009 Nov 22.
8
A review of 3D vessel lumen segmentation techniques: models, features and extraction schemes.三维血管管腔分割技术综述：模型、特征和提取方案。
Med Image Anal. 2009 Dec;13(6):819-45. doi: 10.1016/j.media.2009.07.011. Epub 2009 Aug 12.
9
Fuzzy energy-based active contours.基于模糊能量的主动轮廓。
IEEE Trans Image Process. 2009 Dec;18(12):2747-55. doi: 10.1109/TIP.2009.2030468. Epub 2009 Aug 18.
10
A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification.利用局部图像特征和k近邻分类对胸部CT中肺结节自动检测进行的大规模评估。
Med Image Anal. 2009 Oct;13(5):757-70. doi: 10.1016/j.media.2009.07.001. Epub 2009 Jul 30.