Review of ground glass opacity detection methods in lung CT images

Lv Linying; Liu Xiabi; Zhou Chunwu; Zhao Xinming; Zhao Yanfeng

doi:10.2174/1573405612666160606104405

Review of ground glass opacity detection methods in lung CT images

Lv Linying^*, Liu Xiabi, Zhou Chunwu, Zhao Xinming, Zhao Yanfeng

^*Corresponding author for this work

School of Computer Science and Technology

Research output: Contribution to journal › Review article › peer-review

3 Citations (Scopus)

Abstract

The automatic detection of Ground-Glass Opacity (GGO) in lung CT images is very useful for early diagnosis of lung cancers. In this paper, we present a study of previous GGO detection methods and summarize a common algorithm framework, which includes three components: preprocessing, candidate extraction and GGO identification. For each component, we discuss the main methods. Also we further describe the evaluation criterion and provide a comparison of the performance of the existing approaches.

Original language	English
Pages (from-to)	20-31
Number of pages	12
Journal	Current Medical Imaging
Volume	13
Issue number	1
DOIs	https://doi.org/10.2174/1573405612666160606104405
Publication status	Published - 1 Feb 2017

Keywords

Computer aided detection (CAD)
GGO detection
GGO nodule
Imaging signs
Lung CT images
Medical imaging

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.2174/1573405612666160606104405

Cite this

Linying, L., Xiabi, L., Chunwu, Z., Xinming, Z., & Yanfeng, Z. (2017). Review of ground glass opacity detection methods in lung CT images. Current Medical Imaging, 13(1), 20-31. https://doi.org/10.2174/1573405612666160606104405

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abstract = "The automatic detection of Ground-Glass Opacity (GGO) in lung CT images is very useful for early diagnosis of lung cancers. In this paper, we present a study of previous GGO detection methods and summarize a common algorithm framework, which includes three components: preprocessing, candidate extraction and GGO identification. For each component, we discuss the main methods. Also we further describe the evaluation criterion and provide a comparison of the performance of the existing approaches.",

keywords = "Computer aided detection (CAD), GGO detection, GGO nodule, Imaging signs, Lung CT images, Medical imaging",

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AU - Linying, Lv

AU - Xiabi, Liu

AU - Chunwu, Zhou

AU - Xinming, Zhao

AU - Yanfeng, Zhao

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N2 - The automatic detection of Ground-Glass Opacity (GGO) in lung CT images is very useful for early diagnosis of lung cancers. In this paper, we present a study of previous GGO detection methods and summarize a common algorithm framework, which includes three components: preprocessing, candidate extraction and GGO identification. For each component, we discuss the main methods. Also we further describe the evaluation criterion and provide a comparison of the performance of the existing approaches.

AB - The automatic detection of Ground-Glass Opacity (GGO) in lung CT images is very useful for early diagnosis of lung cancers. In this paper, we present a study of previous GGO detection methods and summarize a common algorithm framework, which includes three components: preprocessing, candidate extraction and GGO identification. For each component, we discuss the main methods. Also we further describe the evaluation criterion and provide a comparison of the performance of the existing approaches.

KW - Computer aided detection (CAD)

KW - GGO detection

KW - GGO nodule

KW - Imaging signs

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Review of ground glass opacity detection methods in lung CT images

Abstract

Keywords

UN SDGs

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