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利用超声图像对甲状腺结节进行分类。

Classification of thyroid nodules using ultrasound images.

作者信息

Manivannan T, Ayyappan Nagarajan

机构信息

Department of Computer Applications, Alagappa University, Karaikudi, Tamilnadu, India.

出版信息

Bioinformation. 2020 Feb 29;16(2):145-148. doi: 10.6026/97320630016145. eCollection 2020.

DOI:10.6026/97320630016145
PMID:32405165
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7196169/
Abstract

Medical imaging using image sensors play an essential role in effective diagnosis. Therefore, it is of interest to use medical imaging techniques for the diagnosis of thyroid-linked dysfunction. Ultrasound is the low-cost image processing technique to study internal organs and blood flow in blood vessels. Digital processed images help to distinguish between normal, benign and malignant tissue stages in organs.Hence, it is of importance to discuss the design and development of a computer-aided image-processing model for thyroid nodule identification, classification and diagnosis.

摘要

使用图像传感器的医学成像在有效诊断中起着至关重要的作用。因此,利用医学成像技术诊断甲状腺相关功能障碍具有重要意义。超声是一种用于研究内部器官和血管内血流的低成本图像处理技术。经过数字处理的图像有助于区分器官中的正常、良性和恶性组织阶段。因此,讨论用于甲状腺结节识别、分类和诊断的计算机辅助图像处理模型的设计与开发具有重要意义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70a7/7196169/42085a27a7a0/97320630016145F1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70a7/7196169/42085a27a7a0/97320630016145F1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70a7/7196169/42085a27a7a0/97320630016145F1.jpg

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本文引用的文献

1
Artificial Intelligence-Based Thyroid Nodule Classification Using Information from Spatial and Frequency Domains.基于人工智能的甲状腺结节分类:利用空间和频域信息
J Clin Med. 2019 Nov 14;8(11):1976. doi: 10.3390/jcm8111976.
2
Additional Value of Superb Microvascular Imaging for Thyroid Nodule Classification with the Thyroid Imaging Reporting and Data System.超声造影在甲状腺影像报告和数据系统分类甲状腺结节中的附加价值。
Ultrasound Med Biol. 2019 Aug;45(8):2040-2048. doi: 10.1016/j.ultrasmedbio.2019.05.001. Epub 2019 May 24.
3
Optimized multi-level elongated quinary patterns for the assessment of thyroid nodules in ultrasound images.
优化的多级拉长五进制模式用于评估超声图像中的甲状腺结节。
Comput Biol Med. 2018 Apr 1;95:55-62. doi: 10.1016/j.compbiomed.2018.02.002. Epub 2018 Feb 7.
4
Texture analysis and machine learning to characterize suspected thyroid nodules and differentiated thyroid cancer: Where do we stand?纹理分析和机器学习在甲状腺结节及分化型甲状腺癌中的特征描述:我们的进展如何?
Eur J Radiol. 2018 Feb;99:1-8. doi: 10.1016/j.ejrad.2017.12.004. Epub 2017 Dec 7.
5
Thyroid Nodule Classification in Ultrasound Images by Fine-Tuning Deep Convolutional Neural Network.通过微调深度卷积神经网络对超声图像中的甲状腺结节进行分类
J Digit Imaging. 2017 Aug;30(4):477-486. doi: 10.1007/s10278-017-9997-y.
6
Application of wavelet techniques for cancer diagnosis using ultrasound images: A Review.基于超声图像的小波技术在癌症诊断中的应用:综述
Comput Biol Med. 2016 Feb 1;69:97-111. doi: 10.1016/j.compbiomed.2015.12.006. Epub 2015 Dec 18.