• 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

关于结肠镜检查图片的结构熵与空间填充因子分析

On Structural Entropy and Spatial Filling Factor Analysis of Colonoscopy Pictures.

作者信息

Nagy Szilvia, Sziová Brigita, Pipek János

机构信息

Széchenyi István University, Egyetem tér 1, H-9026 Gyor, Hungary.

Budapest University of Technology and Economics, Budafoki út 8, H-1111 Budapest, Hungary.

出版信息

Entropy (Basel). 2019 Mar 6;21(3):256. doi: 10.3390/e21030256.

DOI:10.3390/e21030256
PMID:33266971
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7514738/
Abstract

Colonoscopy is the standard device for diagnosing colorectal cancer, which develops from little lesions on the bowel wall called polyps. The Rényi entropies-based structural entropy and spatial filling factor are two scale- and resolution-independent quantities that characterize the shape of a probability distribution with the help of characteristic curves of the structural entropy-spatial filling factor map. This alternative definition of structural entropy is easy to calculate, independent of the image resolution, and does not require the calculation of neighbor statistics, unlike the other graph-based structural entropies.The distant goal of this study was to help computer aided diagnosis in finding colorectal polyps by making the Rényi entropy based structural entropy more understood. The direct goal was to determine characteristic curves that can differentiate between polyps and other structure on the picture. After analyzing the distribution of colonoscopy picture color channels, the typical structures were modeled with simple geometrical functions and the structural entropy-spatial filling factor characteristic curves were determined for these model structures for various parameter sets. A colonoscopy image analying method, i.e., the line- or column-wise scanning of the picture, was also tested, with satisfactory matching of the characteristic curve and the image.

摘要

结肠镜检查是诊断结直肠癌的标准手段，结直肠癌由肠壁上称为息肉的小病变发展而来。基于雷尼熵的结构熵和空间填充因子是两个与尺度和分辨率无关的量，它们借助结构熵-空间填充因子图的特征曲线来表征概率分布的形状。这种结构熵的替代定义易于计算，与图像分辨率无关，并且与其他基于图形的结构熵不同，不需要计算邻域统计量。本研究的长远目标是通过使基于雷尼熵的结构熵更容易理解，来帮助计算机辅助诊断发现结直肠息肉。直接目标是确定能够区分息肉和图片上其他结构的特征曲线。在分析了结肠镜检查图片颜色通道的分布后，用简单几何函数对典型结构进行建模，并针对这些模型结构在各种参数集下确定了结构熵-空间填充因子特征曲线。还测试了一种结肠镜图像分析方法，即逐行或逐列扫描图片，特征曲线与图像的匹配效果令人满意。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/b369af90b3d5/entropy-21-00256-g0A27.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/49863f70d78b/entropy-21-00256-g0A1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/6edad115a01c/entropy-21-00256-g0A2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/38ec222bb54a/entropy-21-00256-g0A3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/1f5ec965c727/entropy-21-00256-g0A4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/461bd111c21e/entropy-21-00256-g0A5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/605c85cf4784/entropy-21-00256-g0A6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/554262c4f1ce/entropy-21-00256-g0A7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/55a282605109/entropy-21-00256-g0A8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/eb51b60b105c/entropy-21-00256-g0A9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/6fad1ac77312/entropy-21-00256-g0A10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/0892c4998d5a/entropy-21-00256-g0A11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/d23ce4cb3639/entropy-21-00256-g0A12.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/3a99732bc83c/entropy-21-00256-g0A13.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/7582308ad45e/entropy-21-00256-g0A14.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/a7b44bd49ec0/entropy-21-00256-g0A15.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/45ed511aed3d/entropy-21-00256-g0A16.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/e0af0c24aa9f/entropy-21-00256-g0A17.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/a99ecd8fd4b0/entropy-21-00256-g0A18.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/d2855c5ac906/entropy-21-00256-g0A19.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/b3fc06b9aabe/entropy-21-00256-g0A20.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/5f65238321ee/entropy-21-00256-g0A21.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/7a8366413daa/entropy-21-00256-g0A22.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/4021026b5467/entropy-21-00256-g0A23.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/3b9f01b49c74/entropy-21-00256-g0A24.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/84f854c5471f/entropy-21-00256-g0A25.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/e9a8e759e99e/entropy-21-00256-g0A26.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/b369af90b3d5/entropy-21-00256-g0A27.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/49863f70d78b/entropy-21-00256-g0A1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/6edad115a01c/entropy-21-00256-g0A2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/38ec222bb54a/entropy-21-00256-g0A3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/1f5ec965c727/entropy-21-00256-g0A4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/461bd111c21e/entropy-21-00256-g0A5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/605c85cf4784/entropy-21-00256-g0A6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/554262c4f1ce/entropy-21-00256-g0A7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/55a282605109/entropy-21-00256-g0A8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/eb51b60b105c/entropy-21-00256-g0A9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/6fad1ac77312/entropy-21-00256-g0A10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/0892c4998d5a/entropy-21-00256-g0A11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/d23ce4cb3639/entropy-21-00256-g0A12.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/3a99732bc83c/entropy-21-00256-g0A13.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/7582308ad45e/entropy-21-00256-g0A14.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/a7b44bd49ec0/entropy-21-00256-g0A15.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/45ed511aed3d/entropy-21-00256-g0A16.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/e0af0c24aa9f/entropy-21-00256-g0A17.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/a99ecd8fd4b0/entropy-21-00256-g0A18.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/d2855c5ac906/entropy-21-00256-g0A19.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/b3fc06b9aabe/entropy-21-00256-g0A20.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/5f65238321ee/entropy-21-00256-g0A21.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/7a8366413daa/entropy-21-00256-g0A22.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/4021026b5467/entropy-21-00256-g0A23.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/3b9f01b49c74/entropy-21-00256-g0A24.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/84f854c5471f/entropy-21-00256-g0A25.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/e9a8e759e99e/entropy-21-00256-g0A26.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3385/7514738/b369af90b3d5/entropy-21-00256-g0A27.jpg

相似文献

1
On Structural Entropy and Spatial Filling Factor Analysis of Colonoscopy Pictures.关于结肠镜检查图片的结构熵与空间填充因子分析
Entropy (Basel). 2019 Mar 6;21(3):256. doi: 10.3390/e21030256.
2
Application of Structural Entropy and Spatial Filling Factor in Colonoscopy Image Classification.结构熵与空间填充因子在结肠镜检查图像分类中的应用
Entropy (Basel). 2021 Jul 22;23(8):936. doi: 10.3390/e23080936.
3
Characterizing time series by extended complexity-entropy curves based on Tsallis, Rényi, and power spectral entropy.基于Tsallis熵、Rényi熵和功率谱熵，通过扩展复杂性-熵曲线对时间序列进行特征描述。
Chaos. 2018 Nov;28(11):113106. doi: 10.1063/1.5038758.
4
Equivalence between four versions of thermostatistics based on strongly pseudoadditive entropies.基于强伪加熵的四种热力统计等价性。
Phys Rev E. 2019 Dec;100(6-1):062135. doi: 10.1103/PhysRevE.100.062135.
5
The Case for Shifting the Rényi Entropy.关于转移雷尼熵的理由。
Entropy (Basel). 2019 Jan 9;21(1):46. doi: 10.3390/e21010046.
6
Low Complexity Estimation Method of Rényi Entropy for Ergodic Sources.遍历源的雷尼熵低复杂度估计方法
Entropy (Basel). 2018 Aug 31;20(9):657. doi: 10.3390/e20090657.
7
The gravity dual of Rényi entropy.Rényi 熵的引力对偶。
Nat Commun. 2016 Aug 12;7:12472. doi: 10.1038/ncomms12472.
8
On Rényi Permutation Entropy.关于雷尼排列熵
Entropy (Basel). 2021 Dec 24;24(1):37. doi: 10.3390/e24010037.
9
Mathematical constraints on a family of biodiversity measures via connections with Rényi entropy.通过与 Renyi 熵的联系，对一组生物多样性测度的数学约束。
Biosystems. 2024 Mar;237:105153. doi: 10.1016/j.biosystems.2024.105153. Epub 2024 Feb 28.
10
A Multiple Rényi Entropy Based Intrusion Detection System for Connected Vehicles.一种基于多重雷尼熵的车联网入侵检测系统
Entropy (Basel). 2020 Feb 6;22(2):186. doi: 10.3390/e22020186.

引用本文的文献

1
Application of Structural Entropy and Spatial Filling Factor in Colonoscopy Image Classification.结构熵与空间填充因子在结肠镜检查图像分类中的应用
Entropy (Basel). 2021 Jul 22;23(8):936. doi: 10.3390/e23080936.
2
Entropy in Image Analysis.图像分析中的熵
Entropy (Basel). 2019 May 17;21(5):502. doi: 10.3390/e21050502.

本文引用的文献

1
Ultrasound Entropy Imaging of Nonalcoholic Fatty Liver Disease: Association with Metabolic Syndrome.非酒精性脂肪性肝病的超声熵成像：与代谢综合征的关联
Entropy (Basel). 2018 Nov 22;20(12):893. doi: 10.3390/e20120893.
2
Improving Entropy Estimates of Complex Network Topology for the Characterization of Coupling in Dynamical Systems.改进用于动态系统耦合特征描述的复杂网络拓扑熵估计
Entropy (Basel). 2018 Nov 20;20(11):891. doi: 10.3390/e20110891.
3
A Brief Review of Generalized Entropies.广义熵简述
Entropy (Basel). 2018 Oct 23;20(11):813. doi: 10.3390/e20110813.
4
Multidimensional and fuzzy sample entropy (SampEn) for quantifying H&E histological images of colorectal cancer.多维和模糊样本熵（SampEn）用于量化结直肠癌的 H&E 组织学图像。
Comput Biol Med. 2018 Dec 1;103:148-160. doi: 10.1016/j.compbiomed.2018.10.013. Epub 2018 Oct 18.
5
Bi-dimensional multiscale entropy: Relation with discrete Fourier transform and biomedical application.二维多尺度熵：与离散傅里叶变换的关系及其在生物医学中的应用。
Comput Biol Med. 2018 Sep 1;100:36-40. doi: 10.1016/j.compbiomed.2018.06.021. Epub 2018 Jun 25.
6
Radiomics Evaluation of Histological Heterogeneity Using Multiscale Textures Derived From 3D Wavelet Transformation of Multispectral Images.使用多光谱图像的三维小波变换衍生的多尺度纹理对组织学异质性进行放射组学评估。
Front Oncol. 2018 Apr 4;8:96. doi: 10.3389/fonc.2018.00096. eCollection 2018.
7
Histopathological image classification with bilinear convolutional neural networks.基于双线性卷积神经网络的组织病理学图像分类
Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:4050-4053. doi: 10.1109/EMBC.2017.8037745.
8
Texture Analysis of Abnormal Cell Images for Predicting the Continuum of Colorectal Cancer.用于预测结直肠癌连续状态的异常细胞图像纹理分析
Anal Cell Pathol (Amst). 2017;2017:8428102. doi: 10.1155/2017/8428102. Epub 2017 Jan 17.
9
Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results From the MICCAI 2015 Endoscopic Vision Challenge.视频结肠镜中息肉检测方法的比较验证：来自 MICCAI 2015 内镜视觉挑战赛的结果。
IEEE Trans Med Imaging. 2017 Jun;36(6):1231-1249. doi: 10.1109/TMI.2017.2664042. Epub 2017 Feb 2.
10
Potential of hybrid adaptive filtering in inflammatory lesion detection from capsule endoscopy images.混合自适应滤波在胶囊内窥镜图像炎症病变检测中的潜力。
World J Gastroenterol. 2016 Oct 21;22(39):8641-8657. doi: 10.3748/wjg.v22.i39.8641.