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: Lung CT images, Imaging signs, GGO nodule, GGO detection, Medical imaging, Computer aided detection (CAD).
Graphical Abstract
Call for Papers in Thematic Issues
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Multispectral and hyperspectral imaging is an interdisciplinary research area, widely focuses on spectral, spatial, and temporal data. It offers a plethora of opportunities to efficiently analyze the vast area of the earth's surface. However, there are numerous factors such as dimensionality and size of the hyperspectral data, lesser training samples, ...read more
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Medical Image Processing regards a set of methodologies that have been developed over recent years with the purpose of improving medical image quality, improving medical data visualization, understanding, and assisting medical diagnosis, and so on. The application of machine and deep learning methods in sensing and imaging can potentially have ...read more
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