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New Advances in Sensing and Artificial Intelligence for Medical Imaging

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

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 3545

Special Issue Editors


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Guest Editor
University of A Coruña, 3CITIC-Research Center of Information and Communication Technologies
Interests: computer vision; biomedical image processing; pattern recognition and medical informatics
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
University of A Coruña, 3CITIC-Research Center of Information and Communication Technologies
Interests: computer vision; medical imaging; pattern recognition; machine learning

E-Mail Website
Guest Editor
3CITIC-Research Center of Information and Communication Technologies, University of A Coruña, A Coruña, Spain
Interests: computer vision; image processing; pattern recognition; biomedical image processing; machine learning
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Universidad Nacional de Educación a Distancia (UNED)
Interests: artificial intelligence; evolutionary computation; artificial vision; machine learning

Special Issue Information

Dear Colleagues,

Over the last several decades, the evolution and spread of medical sensors and devices for image acquisition increased drastically, revolutionizing many analysis and diagnostic processes of different healthcare services. Specifically, procedures such as screening, diagnosis, and treatment, among others, were improved and complemented with more and better information to facilitate and reinforce a satisfactory clinical performance. On the other hand, this spread of capture devices and sensing implies the analysis and processing of greater amounts of information, a context where computerized solutions are highly desirable to automatically or semi-automatically process considerable amounts of information to facilitate the work of the specialists. In the last several years, the use of signal processing and artificial intelligence in medical imaging have played a crucial role in exploiting the availability of huge amounts of information, improving medical analysis and diagnostics.

This Special Issue is focused on the recent advances in sensing and capture devices for medical imaging as well as proposals for image analysis and especially artificial intelligence for computer-aided biomedical imaging. Given that, contributions are welcome on topics that include but are not limited to:

Biomedical sensors

Integrated medical sensors

Sensors for telemedicine

Wearable sensors for medical applications

Biomedical image analysis

Deep learning in biomedicine

Artificial Intelligence in biomedicine

Applied soft computing

Computer-assisted diagnosis

Image-guided therapy

Image-guided surgery and intervention

Motion analysis

Telemedicine with medical images

Biomedical robotics and haptics

Prof. Marcos Ortega Hortas
Prof. Manuel F. González Penedo
Dr. Jorge Novo Buján
Dr. Enrique Carmona Suárez
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

  • Biomedical sensors
  • Integrated medical sensors
  • Sensors for telemedicine
  • Wearable sensors for medical applications
  • Biomedical image analysis
  • Deep learning in biomedicine
  • Artificial Intelligence in biomedicine
  • Applied soft computing
  • Computer-assisted diagnosis
  • Image-guided therapy
  • Image-guided surgery and intervention
  • Motion analysis
  • Telemedicine with medical images
  • Biomedical robotics and haptics

Published Papers (1 paper)

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Research

18 pages, 7267 KiB  
Article
Automatic Detection of Freshwater Phytoplankton Specimens in Conventional Microscopy Images
by David Rivas-Villar, José Rouco, Manuel G. Penedo, Rafael Carballeira and Jorge Novo
Sensors 2020, 20(22), 6704; https://0-doi-org.brum.beds.ac.uk/10.3390/s20226704 - 23 Nov 2020
Cited by 4 | Viewed by 2901
Abstract
Water safety and quality can be compromised by the proliferation of toxin-producing phytoplankton species, requiring continuous monitoring of water sources. This analysis involves the identification and counting of these species which requires broad experience and knowledge. The automatization of these tasks is highly [...] Read more.
Water safety and quality can be compromised by the proliferation of toxin-producing phytoplankton species, requiring continuous monitoring of water sources. This analysis involves the identification and counting of these species which requires broad experience and knowledge. The automatization of these tasks is highly desirable as it would release the experts from tedious work, eliminate subjective factors, and improve repeatability. Thus, in this preliminary work, we propose to advance towards an automatic methodology for phytoplankton analysis in digital images of water samples acquired using regular microscopes. In particular, we propose a novel and fully automatic method to detect and segment the existent phytoplankton specimens in these images using classical computer vision algorithms. The proposed method is able to correctly detect sparse colonies as single phytoplankton candidates, thanks to a novel fusion algorithm, and is able to differentiate phytoplankton specimens from other image objects in the microscope samples (like minerals, bubbles or detritus) using a machine learning based approach that exploits texture and colour features. Our preliminary experiments demonstrate that the proposed method provides satisfactory and accurate results. Full article
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