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Applied Sciences
Special Issues
Hyperspectral Imaging: Methods and Applications II
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Hyperspectral Imaging: Methods and Applications II

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Optics and Lasers".

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 3937

Special Issue Editors

Prof. Dick Sterenborg

E-Mail Website
Guest Editor
Department of Biomedical Engineering and Physics, Academic Medical Center, 1105 AZ Amsterdam, The Netherlands
Interests: hyperspectral imaging; biomedical optics; optical biopsy; fiber optic spectroscopy
Special Issues, Collections and Topics in MDPI journals
Dr. Behdad Dasht Bozorg

E-Mail Website
Guest Editor
Department of Surgical Oncology, Nederlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
Interests: hyperspectral Imaging; image guided treatment; medical image analysis; machine learning; computer vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

HSI is a novel and versatile optical imaging technology that is fundamentally safe or harmless and can be applied on virtually any type of sample, tough or fragile, close or distant, dead or alive. It has great potential for rapid, non-destructive material investigation.

HSI is currently a hot topic in many fields of research: from medicine to forensic science and from biology and materials science to archeology. It is quite extraordinary to see researchers from very different fields and with different backgrounds use this technology for very diverse problems. Often, however, they encounter comparable problems and face similar challenges.

The Special Issue “Hyperspectral Imaging, Methods and Applications” is dedicated to bringing papers from the different fields together and stimulate cross-pollination. Therefore, we welcome papers from all fields of science. A special focus is on design and methodology; specialized optics, data acquisition protocols or processing schemes, algorithm development, as well as novel applications of HSI or application methods for HSI.

Prof. Dick Sterenborg
Dr. Behdad Dasht Bozorg
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. Applied Sciences 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 2400 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
  • diffuse reflectance
  • machine learning

Published Papers (2 papers)

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Research

19 pages, 10570 KiB  
Article
Feasibility of Ex Vivo Margin Assessment with Hyperspectral Imaging during Breast-Conserving Surgery: From Imaging Tissue Slices to Imaging Lumpectomy Specimen
by Esther Kho, Behdad Dashtbozorg, Joyce Sanders, Marie-Jeanne T. F. D. Vrancken Peeters, Frederieke van Duijnhoven, Henricus J. C. M. Sterenborg and Theo J. M. Ruers
Appl. Sci. 2021, 11(19), 8881; https://0-doi-org.brum.beds.ac.uk/10.3390/app11198881 - 24 Sep 2021
Cited by 6 | Viewed by 1541
Abstract
Developing algorithms for analyzing hyperspectral images as an intraoperative tool for margin assessment during breast-conserving surgery requires a dataset with reliable histopathologic labels. The feasibility of using tissue slices hyperspectral dataset with a high correlation with histopathology for developing an algorithm for analyzing [...] Read more.
Developing algorithms for analyzing hyperspectral images as an intraoperative tool for margin assessment during breast-conserving surgery requires a dataset with reliable histopathologic labels. The feasibility of using tissue slices hyperspectral dataset with a high correlation with histopathology for developing an algorithm for analyzing the images from the surface of lumpectomy specimens was investigated. We presented a method to acquire hyperspectral images from the lumpectomy surface with a high correlation with histopathology. The tissue slices dataset was compared with the dataset obtained on lumpectomy specimen and the wavelengths with a penetration depth up to the minimum sample thickness of the tissue slices were used to develop a tissue classification algorithm. Spectral differences were observed between tissue slices and lumpectomy datasets due to differences in the sample thickness between both datasets; wavelengths with a high penetration depth were able to penetrate through the thinner tissue slices, affecting the captured signal. By using only wavelengths with a penetration depth up to the minimum sample thickness of the tissue slices, the adipose tissue could be discriminated from other tissue types, but differentiating malignant from connective tissue was more challenging. Full article
(This article belongs to the Special Issue Hyperspectral Imaging: Methods and Applications II)
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13 pages, 4558 KiB  
Article
Coupled Convolutional Neural Network-Based Detail Injection Method for Hyperspectral and Multispectral Image Fusion
by Xiaochen Lu, Dezheng Yang, Fengde Jia and Yifeng Zhao
Appl. Sci. 2021, 11(1), 288; https://0-doi-org.brum.beds.ac.uk/10.3390/app11010288 - 30 Dec 2020
Cited by 4 | Viewed by 1863
Abstract
In this paper, a detail-injection method based on a coupled convolutional neural network (CNN) is proposed for hyperspectral (HS) and multispectral (MS) image fusion with the goal of enhancing the spatial resolution of HS images. Owing to the excellent performance in spectral fidelity [...] Read more.
In this paper, a detail-injection method based on a coupled convolutional neural network (CNN) is proposed for hyperspectral (HS) and multispectral (MS) image fusion with the goal of enhancing the spatial resolution of HS images. Owing to the excellent performance in spectral fidelity of the detail-injection model and the image spatial–spectral feature exploration ability of CNN, the proposed method utilizes a couple of CNN networks as the feature extraction method and learns details from the HS and MS images individually. By appending an additional convolutional layer, both the extracted features of two images are concatenated to predict the missing details of the anticipated HS image. Experiments on simulated and real HS and MS data show that compared with some state-of-the-art HS and MS image fusion methods, our proposed method achieves better fusion results, provides excellent spectrum preservation ability, and is easy to implement. Full article
(This article belongs to the Special Issue Hyperspectral Imaging: Methods and Applications II)
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