Medical hyperspectral imaging: a review

By Guolan Lu, Baowei Fei. Journal of Biomedical Optics, 2014

2,016 citations recorded by the source corpus.

Abstract. Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications, especially in disease diagnosis and image-guided surgery. HSI acquires a three-dimensional dataset called hypercube, with two spatial dimensions and one spectral dimension. Spatially resolved spectral imaging obtained by HSI provides diagnostic information about the tissue physiology, morphology, and composition. This review paper presents an overview of the literature on medical hyperspectral imaging technology and its applications. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application.

Subfields: Biomedical Engineering, Biophysics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/3da01deee8c6011e27cc92b26a521cd082bbddd4

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In Biomedical Engineering

In Biophysics

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Analyzing Inverse Problems with Invertible Neural Networks (2018, background): “Such changes influence the reflectance of the tissue, which can be measured by multispectral cameras [27].”
MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction (2022, background): “Because of this advantage, HSIs have wide applications such as medical image processing [8, 51, 57], remote sensing [10, 54, 82], object tracking [35, 61], and so”
Spatial-Spectral Transformer for Hyperspectral Image Classification (2021, background): “Thus, the obtained abundant information makes HSI useful in many areas including effective measurement of agricultural performance [3], plant diseases detection [4], identification of minerals [5], disease diagnosis and image-guided surgery [6], ecosystem measurement [7], and earth monitoring [8].”
A review of neural networks in plant disease detection using hyperspectral data (2018, methodology): “This was a signiﬁcant application in diagnostic hyperspectral imaging [70].”
A Review on a Deep Learning Perspective in Brain Cancer Classification (2019, background): “HSI uses a wider range of the electromagnetic spectrum compared to normal three channel Red, Green and Blue (RGB) image type [41], which provides detailed information about tissues in the captured scene [42].”
Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review (2019, background): “Likewise for RS, where the cover-type classiﬁcation task is the prominent application, classiﬁcation operated on the surface of different kinds of specimens, acquired through HSI systems at various scales (from micro to macro), is gaining high interest [3].”
Discrimination between abiotic and biotic drought stress in tomatoes using hyperspectral imaging (2018, methodology): “Apart from geology-oriented applications, this technology has since inception been successfully used in many other areas such as microscopy [8], material classification [9], medical diagnostics [10], artwork and historical artefact analysis and conservation [11], and especially, food quality and saf”
Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction (2021, background): “, remote sensing [5, 31, 42, 60], object tracking [19, 37, 38], medical image processing [3,30,34], etc.”
AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020, neutral): “to gain a better understanding of the reflectance properties of the object being imaged [39].”