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Hyperspectral/Multispectral Imaging Sensing Techniques and Their Medical Applications
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Hyperspectral/Multispectral Imaging Sensing Techniques and Their Medical Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 13186

Special Issue Editors

Dr. Samuel Ortega

E-Mail Website
Guest Editor
Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), 35017 Las Palmas de Gran Canaria, Spain
Interests: hyperspectral imaging; machine learning; deep learning; image processing; hyperspectral pathology; medical hyperspectral microscopy
Prof. Gustavo Marrero-Callico

E-Mail Website
Guest Editor
Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), Las Palmas de Gran Canaria, Spain
Interests: hyperspectral image processing; artificial intelligence algorithms; hardware architectures for real-time image processing; super-resolution image enhancement; circuits for multimedia processing and video coding standards; computer microarchitecture; synthesis-based design for SOCs; hardware–software codesign
Special Issues, Collections and Topics in MDPI journals
Dr. Himar Fabelo

E-Mail Website
Guest Editor
Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), 35017 Las Palmas de Gran Canaria, Spain
Interests: hyperspectral imaging; brain cancer; machine learning; algorithm development and acceleration; medical hyperspectral intraoperative instrumentation
Prof. Baowei Fei

E-Mail Website
Guest Editor
1. Department of Bioengineering, University of Texas at Dallas, Richardson, TX, USA
2. Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
Interests: biomedical imaging; artificial intelligence (AI) and machine learning; quantitative imaging: image-guided interventions; translational imaging; multimodality imaging; virtual reality, augmented reality, mixed reality for biomedical and clinical applications; cancer research; neurodegenerative disorders and cardiovascular diseases

Special Issue Information

Dear Colleagues,

The study of light propagation through biological tissues has been demonstrated to be useful to identify several diseases. Light propagation in biological tissue involves three different photophysical processes: refraction, scattering, and absorption. These properties of interaction between light and biological tissue motivate the use of technologies that exploit the information of light propagation through tissue to develop novel decision support tools for clinical diagnosis. Hyperspectral and multispectral imaging (HSI/MSI) are optical spectroscopy imaging modalities that directly measure the incoming radiance spectra of light. There are two major detection modes depending on the incidence of light within the tissue: light reflection or light transmission. The spectral information measured by these modalities is usually related to the scattering and absorption properties of light within the sample but can also be used for fluorescence measurements. In this sense, HSI/MSI are able to obtain both spatial and spectral information within and beyond human visual sensitivity, which is restricted to the spectral range from 380 to 740 nm. HSI/MSI can obtain richer information within the electromagnetic spectrum by capturing information regarding different wavelengths (also called spectral bands or spectral channels) up to 2500 nm or beyond.

In recent years, HSI/MSI technology has attracted the interest of many researchers from the medical field, where several studies have implemented this technology for automated disease diagnosis and image-guided surgery. This Special Issue is devoted to collecting novel research in the use of HSI/MSI for diverse medical applications.

Dr. Samuel Ortega
Prof. Gustavo Marrero-Callico
Dr. Himar Fabelo
Prof. Baowei Fei
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Hyperspectral imaging
  • Multispectral imaging
  • Biomedical applications
  • Instrumentation development or characterization
  • Medical image processing
  • Machine learning
  • Deep learning
  • Image-guided surgery
  • Non-invasive diagnosis
  • Healthcare applications

Published Papers (5 papers)

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Research

23 pages, 5034 KiB  
Article
Towards Real-Time Hyperspectral Multi-Image Super-Resolution Reconstruction Applied to Histological Samples
by Carlos Urbina Ortega, Eduardo Quevedo Gutiérrez, Laura Quintana, Samuel Ortega, Himar Fabelo, Lucana Santos Falcón and Gustavo Marrero Callico
Sensors 2023, 23(4), 1863; https://0-doi-org.brum.beds.ac.uk/10.3390/s23041863 - 07 Feb 2023
Cited by 1 | Viewed by 1571
Abstract
Hyperspectral Imaging (HSI) is increasingly adopted in medical applications for the usefulness of understanding the spectral signature of specific organic and non-organic elements. The acquisition of such images is a complex task, and the commercial sensors that can measure such images is scarce [...] Read more.
Hyperspectral Imaging (HSI) is increasingly adopted in medical applications for the usefulness of understanding the spectral signature of specific organic and non-organic elements. The acquisition of such images is a complex task, and the commercial sensors that can measure such images is scarce down to the point that some of them have limited spatial resolution in the bands of interest. This work proposes an approach to enhance the spatial resolution of hyperspectral histology samples using super-resolution. As the data volume associated to HSI has always been an inconvenience for the image processing in practical terms, this work proposes a relatively low computationally intensive algorithm. Using multiple images of the same scene taken in a controlled environment (hyperspectral microscopic system) with sub-pixel shifts between them, the proposed algorithm can effectively enhance the spatial resolution of the sensor while maintaining the spectral signature of the pixels, competing in performance with other state-of-the-art super-resolution techniques, and paving the way towards its use in real-time applications. Full article
(This article belongs to the Special Issue Hyperspectral/Multispectral Imaging Sensing Techniques and Their Medical Applications)
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19 pages, 16162 KiB  
Article
Evaluation of Preprocessing Methods on Independent Medical Hyperspectral Databases to Improve Analysis
by Beatriz Martinez-Vega, Mariia Tkachenko, Marianne Matkabi, Samuel Ortega, Himar Fabelo, Francisco Balea-Fernandez, Marco La Salvia, Emanuele Torti, Francesco Leporati, Gustavo M. Callico and Claire Chalopin
Sensors 2022, 22(22), 8917; https://0-doi-org.brum.beds.ac.uk/10.3390/s22228917 - 18 Nov 2022
Cited by 4 | Viewed by 2172
Abstract
Currently, one of the most common causes of death worldwide is cancer. The development of innovative methods to support the early and accurate detection of cancers is required to increase the recovery rate of patients. Several studies have shown that medical Hyperspectral Imaging [...] Read more.
Currently, one of the most common causes of death worldwide is cancer. The development of innovative methods to support the early and accurate detection of cancers is required to increase the recovery rate of patients. Several studies have shown that medical Hyperspectral Imaging (HSI) combined with artificial intelligence algorithms is a powerful tool for cancer detection. Various preprocessing methods are commonly applied to hyperspectral data to improve the performance of the algorithms. However, there is currently no standard for these methods, and no studies have compared them so far in the medical field. In this work, we evaluated different combinations of preprocessing steps, including spatial and spectral smoothing, Min-Max scaling, Standard Normal Variate normalization, and a median spatial smoothing technique, with the goal of improving tumor detection in three different HSI databases concerning colorectal, esophagogastric, and brain cancers. Two machine learning and deep learning models were used to perform the pixel-wise classification. The results showed that the choice of preprocessing method affects the performance of tumor identification. The method that showed slightly better results with respect to identifing colorectal tumors was Median Filter preprocessing (0.94 of area under the curve). On the other hand, esophagogastric and brain tumors were more accurately identified using Min-Max scaling preprocessing (0.93 and 0.92 of area under the curve, respectively). However, it is observed that the Median Filter method smooths sharp spectral features, resulting in high variability in the classification performance. Therefore, based on these results, obtained with different databases acquired by different HSI instrumentation, the most relevant preprocessing technique identified in this work is Min-Max scaling. Full article
(This article belongs to the Special Issue Hyperspectral/Multispectral Imaging Sensing Techniques and Their Medical Applications)
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12 pages, 2863 KiB  
Article
Deep Convolutional Generative Adversarial Networks to Enhance Artificial Intelligence in Healthcare: A Skin Cancer Application
by Marco La Salvia, Emanuele Torti, Raquel Leon, Himar Fabelo, Samuel Ortega, Beatriz Martinez-Vega, Gustavo M. Callico and Francesco Leporati
Sensors 2022, 22(16), 6145; https://0-doi-org.brum.beds.ac.uk/10.3390/s22166145 - 17 Aug 2022
Cited by 9 | Viewed by 2837
Abstract
In recent years, researchers designed several artificial intelligence solutions for healthcare applications, which usually evolved into functional solutions for clinical practice. Furthermore, deep learning (DL) methods are well-suited to process the broad amounts of data acquired by wearable devices, smartphones, and other sensors [...] Read more.
In recent years, researchers designed several artificial intelligence solutions for healthcare applications, which usually evolved into functional solutions for clinical practice. Furthermore, deep learning (DL) methods are well-suited to process the broad amounts of data acquired by wearable devices, smartphones, and other sensors employed in different medical domains. Conceived to serve the role of diagnostic tool and surgical guidance, hyperspectral images emerged as a non-contact, non-ionizing, and label-free technology. However, the lack of large datasets to efficiently train the models limits DL applications in the medical field. Hence, its usage with hyperspectral images is still at an early stage. We propose a deep convolutional generative adversarial network to generate synthetic hyperspectral images of epidermal lesions, targeting skin cancer diagnosis, and overcome small-sized datasets challenges to train DL architectures. Experimental results show the effectiveness of the proposed framework, capable of generating synthetic data to train DL classifiers. Full article
(This article belongs to the Special Issue Hyperspectral/Multispectral Imaging Sensing Techniques and Their Medical Applications)
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21 pages, 7740 KiB  
Article
Intraoperative Optical Monitoring of Spinal Cord Hemodynamics Using Multiwavelength Imaging System
by Nicolas Mainard, Olivier Tsiakaka, Songlin Li, Julien Denoulet, Karim Messaoudene, Raphael Vialle and Sylvain Feruglio
Sensors 2022, 22(10), 3840; https://0-doi-org.brum.beds.ac.uk/10.3390/s22103840 - 19 May 2022
Viewed by 2247
Abstract
The spinal cord is a major structure of the central nervous system allowing, among other things, the transmission of afferent sensory and efferent motor information. During spinal surgery, such as scoliosis correction, this structure can be damaged, resulting in major neurological damage to [...] Read more.
The spinal cord is a major structure of the central nervous system allowing, among other things, the transmission of afferent sensory and efferent motor information. During spinal surgery, such as scoliosis correction, this structure can be damaged, resulting in major neurological damage to the patient. To date, there is no direct way to monitor the oxygenation of the spinal cord intraoperatively to reflect its vitality. This is essential information that would allow surgeons to adapt their procedure in case of ischemic suffering of the spinal cord. We report the development of a specific device to monitor the functional status of biological tissues with high resolution. The device, operating with multiple wavelengths, uses Near-InfraRed Spectroscopy (NIRS) in combination with other additional sensors, including ElectroNeuroGraphy (ENG). In this paper, we focused primarily on aspects of the PhotoPlethysmoGram (PPG), emanating from four different light sources to show in real time and record biological signals from the spinal cord in transmission and reflection modes. This multispectral system was successfully tested in in vivo experiments on the spinal cord of a pig for specific medical applications. Full article
(This article belongs to the Special Issue Hyperspectral/Multispectral Imaging Sensing Techniques and Their Medical Applications)
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19 pages, 38535 KiB  
Article
Prediction of In Vivo Laser-Induced Thermal Damage with Hyperspectral Imaging Using Deep Learning
by Martina De Landro, Eric Felli, Toby Collins, Richard Nkusi, Andrea Baiocchini, Manuel Barberio, Annalisa Orrico, Margherita Pizzicannella, Alexandre Hostettler, Michele Diana and Paola Saccomandi
Sensors 2021, 21(20), 6934; https://0-doi-org.brum.beds.ac.uk/10.3390/s21206934 - 19 Oct 2021
Cited by 16 | Viewed by 2894
Abstract
Thermal ablation is an acceptable alternative treatment for primary liver cancer, of which laser ablation (LA) is one of the least invasive approaches, especially for tumors in high-risk locations. Precise control of the LA effect is required to safely destroy the tumor. Although [...] Read more.
Thermal ablation is an acceptable alternative treatment for primary liver cancer, of which laser ablation (LA) is one of the least invasive approaches, especially for tumors in high-risk locations. Precise control of the LA effect is required to safely destroy the tumor. Although temperature imaging techniques provide an indirect measurement of the thermal damage, a degree of uncertainty remains about the treatment effect. Optical techniques are currently emerging as tools to directly assess tissue thermal damage. Among them, hyperspectral imaging (HSI) has shown promising results in image-guided surgery and in the thermal ablation field. The highly informative data provided by HSI, associated with deep learning, enable the implementation of non-invasive prediction models to be used intraoperatively. Here we show a novel paradigm “peak temperature prediction model” (PTPM), convolutional neural network (CNN)-based, trained with HSI and infrared imaging to predict LA-induced damage in the liver. The PTPM demonstrated an optimal agreement with tissue damage classification providing a consistent threshold (50.6 ± 1.5 °C) for the damage margins with high accuracy (~0.90). The high correlation with the histology score (r = 0.9085) and the comparison with the measured peak temperature confirmed that PTPM preserves temperature information accordingly with the histopathological assessment. Full article
(This article belongs to the Special Issue Hyperspectral/Multispectral Imaging Sensing Techniques and Their Medical Applications)
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