Literature DB >> 33383204

A review of the medical hyperspectral imaging systems and unmixing algorithms' in biological tissues.

Aziz Ul Rehman1, Shahzad Ahmad Qureshi2.   

Abstract

Hyperspectral fluorescence imaging (HFI) is a well-known technique in the medical research field and is considered a non-invasive tool for tissue diagnosis. This review article gives a brief introduction to acquisition methods, including the image preprocessing methods, feature selection and extraction methods, data classification techniques and medical image analysis along with recent relevant references. The process of fusion of unsupervised unmixing techniques with other classification methods, like the combination of support vector machine with an artificial neural network, the latest snapshot Hyperspectral imaging (HSI) and vortex analysis techniques are also outlined. Finally, the recent applications of hyperspectral images in cellular differentiation of various types of cancer are discussed.
Copyright © 2020 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Breast cancer; Deep learning; Hyper-spectral image classification; Hyper-spectral imaging system; Hyper-spectral imaging techniques hyperspectral image applications; Lung cancer; Ophthalmology; Support vector machines; Unmixing algorithms

Mesh:

Substances:

Year:  2020        PMID: 33383204     DOI: 10.1016/j.pdpdt.2020.102165

Source DB:  PubMed          Journal:  Photodiagnosis Photodyn Ther        ISSN: 1572-1000            Impact factor:   3.631


  3 in total

Review 1.  Live-cell fluorescence spectral imaging as a data science challenge.

Authors:  Jessy Pamela Acuña-Rodriguez; Jean Paul Mena-Vega; Orlando Argüello-Miranda
Journal:  Biophys Rev       Date:  2022-03-23

Review 2.  An Overview of the Successful Application of Vibrational Spectroscopy Techniques to Quantify Nutraceuticals in Fruits and Plants.

Authors:  Daniel Cozzolino
Journal:  Foods       Date:  2022-01-24

3.  Hyperspectral Imaging during Normothermic Machine Perfusion-A Functional Classification of Ex Vivo Kidneys Based on Convolutional Neural Networks.

Authors:  Florian Sommer; Bingrui Sun; Julian Fischer; Miriam Goldammer; Christine Thiele; Hagen Malberg; Wenke Markgraf
Journal:  Biomedicines       Date:  2022-02-07
  3 in total

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