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Image and Signal Processing in Biomedical Engineering

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

Deadline for manuscript submissions: closed (20 January 2023) | Viewed by 1884

Special Issue Editor


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Guest Editor
Department of Biomedical Engineering, Chungnam National University College of Medicine, Daejeon 35015, Republic of Korea
Interests: medical image analysis; biosignal analysis; deep learning; extended reality
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Most of the unstructured medical data is occupied by image and signal data, and processing these types of data is a major field in biomedical engineering.

Especially recently, methods of processing image and signal are rapidly developing with the development of machine learning and deep learning techniques.

Therefore, the purpose of this Special Issue is intended for the presentation of novel ideas, and to present studies that have verified the results through experiments in application fields using medical image and signal data.

Areas relevant to image and signal processing in biomedical engineering, but are not limited to, computer aided diagnosis, applications of big data in medicine, artificial intelligence, machine learning and deep learning and other sources.

Dr. Dongheon Lee
Guest Editor

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

  • biomedical imaging and image processing
  • computer aided diagnosis
  • image segmentation, registration
  • biomedical signal processing
  • applications of big data in medicine
  • artificial intelligence, machine learning and deep learning

Published Papers (1 paper)

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Research

15 pages, 3612 KiB  
Article
Assessment of Cranial Deformation Indices by Automatic Smartphone-Based Photogrammetric Modelling
by Sergio Baselga, Gaspar Mora-Navarro and José Luis Lerma
Appl. Sci. 2022, 12(22), 11499; https://doi.org/10.3390/app122211499 - 12 Nov 2022
Cited by 2 | Viewed by 1525
Abstract
This paper presents research carried out to assess the accuracy of a fully automatic smartphone-based photogrammetric solution (PhotoMeDAS) to obtain a cranial diagnostic based on the 3D head model. The rigorous propagation of the coordinate measurement uncertainty to the infant’s derived cranial deformation [...] Read more.
This paper presents research carried out to assess the accuracy of a fully automatic smartphone-based photogrammetric solution (PhotoMeDAS) to obtain a cranial diagnostic based on the 3D head model. The rigorous propagation of the coordinate measurement uncertainty to the infant’s derived cranial deformation indices is demonstrated. The cranial anthropometric parameters and cranial deformation indices that PhotoMeDAS calculates automatically were analysed based on the estimated accuracy and uncertainty. To obtain both accuracy and uncertainty, a dummy head was measured 54 times under different conditions. The same head was measured with a top-of-the-line coordinate-measuring machine (CMM), and the results were used as ground-truth data. It is demonstrated that the PhotoMeDAS 3D models are an average of 1.01 times bigger than the corresponding ground truth, and the uncertainties are around 1 mm. Even assuming uncertainties in the coordinates of up to 1.5 mm, the error in the derived deformation index uncertainties is around 1%. In conclusion, the PhotoMeDAS solution improves the uncertainty obtained in an ordinary paediatric consultation and can be recommended as a tool for doctors to establish an adequate medical diagnosis based on comprehensive cranial deformation indices, which is much more precise and complete than the information obtained by existing analogue devices (measuring tapes and callipers) and easier to use and less expensive than radiological imaging (CT and MRI). Full article
(This article belongs to the Special Issue Image and Signal Processing in Biomedical Engineering)
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