Special Issue "Advanced Medical Signal Processing and Visualization"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: 20 January 2023 | Viewed by 1074

Special Issue Editors

Prof. Dr. Jiann-Der Lee
E-Mail Website
Guest Editor
Department of Electrical Engineering, Chang Gung University, Tao-Yuan 33302, Taiwan
Interests: medical imaging processing; pattern recognition; computer visualization; VLSI design
Special Issues, Collections and Topics in MDPI journals
Dr. Jong-Chih Chien
E-Mail Website
Guest Editor
School of Informatics, Kainan University, Tao-Yuan 33857, Taiwan
Interests: digital image processing; artificial intelligence; machine vision; digital signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

This Special Issue of Applied Sciences seeks submissions on new and interesting ways to process and visualize medical data. Any research into methods to process medical data and to visualize results to help to improve medical diagnoses or procedures, help doctors to communicate difficult ideas to patients, or educate the next generation of physicians or scientists is the focus of this Special Issue. Visualization methods such as VR/AR/MR or any other interesting methods are welcome. The purpose of most types of medical data is to bring what is not readily visible or discernable, but important for patients’ health issues, into the attention of physicians in order to avoid the worst-case scenarios. Even though this Special Issue is only a small step toward improving how medical data are processed and viewed, it is our hope that future generations will find the results presented in the papers published in this Special Issue to be useful and will continue to improve upon them.

Prof. Dr. Jiann-Der Lee
Dr. Jong-Chih Chien
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 2300 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.

Published Papers (3 papers)

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Research

Article
Advanced Analysis of Electroretinograms Based on Wavelet Scalogram Processing
Appl. Sci. 2022, 12(23), 12365; https://doi.org/10.3390/app122312365 (registering DOI) - 02 Dec 2022
Viewed by 179
Abstract
The electroretinography (ERG) is a diagnostic test that measures the electrical activity of the retina in response to a light stimulus. The current ERG signal analysis uses four components, namely amplitude, and the latency of a-wave and b-wave. Nowadays, the international electrophysiology community [...] Read more.
The electroretinography (ERG) is a diagnostic test that measures the electrical activity of the retina in response to a light stimulus. The current ERG signal analysis uses four components, namely amplitude, and the latency of a-wave and b-wave. Nowadays, the international electrophysiology community established the standard for electroretinography in 2008. However, in terms of signal analysis, there were no major changes. ERG analysis is still based on a four-component evaluation. The article describes the ERG database, including the classification of signals via the advanced analysis of electroretinograms based on wavelet scalogram processing. To implement an extended analysis of the ERG, the parameters extracted from the wavelet scalogram of the signal were obtained using digital image processing and machine learning methods. Specifically, the study focused on the preprocessing of wavelet scalogram as images, and the extraction of connected components and thier evaluation. As a machine learning method, a decision tree was selected as one that incorporated feature selection. The study results show that the proposed algorithm more accurately implements the classification of adult electroretinogram signals by 19%, and pediatric signals by 20%, in comparison with the classical features of ERG. The promising use of ERG is presented using differential diagnostics, which may also be used in preclinical toxicology and experimental modeling. The problem of developing methods for electrophysiological signals analysis in ophthalmology is associated with the complex morphological structures of electrophysiological signal components. Full article
(This article belongs to the Special Issue Advanced Medical Signal Processing and Visualization)
Article
A Projection-Based Augmented Reality System for Medical Applications
Appl. Sci. 2022, 12(23), 12027; https://doi.org/10.3390/app122312027 - 24 Nov 2022
Viewed by 350
Abstract
The aim of this paper was to present the development of an Augmented Reality (AR) system which uses a 2D video projector to project a 3D model of blood vessels, built by combining Computed Tomography (CT) slices of a human brain, onto a [...] Read more.
The aim of this paper was to present the development of an Augmented Reality (AR) system which uses a 2D video projector to project a 3D model of blood vessels, built by combining Computed Tomography (CT) slices of a human brain, onto a model of a human head. The difficulty in building this system is that the human head contains, not flat surfaces, but non-regular curved surfaces. Using a 2D projector to project a 3D model onto non-regular curved 3D surfaces would result in serious distortions of the projection if the image was not uncorrected first. This paper proposed a method of correcting the projection, not only based on the curvatures of the surfaces, but also on the viewing position of the observer. Experimental results of this system showed that an average positional deviation error of 2.065 mm could be achieved under various test conditions. Full article
(This article belongs to the Special Issue Advanced Medical Signal Processing and Visualization)
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Article
Prediagnosis of Heart Failure (HF) Using Deep Learning and the Korotkoff Sound
Appl. Sci. 2022, 12(20), 10322; https://doi.org/10.3390/app122010322 - 13 Oct 2022
Viewed by 384
Abstract
Heart failure (HF) is a devastating condition that impairs people’s lives and health. Because of the high morbidity and mortality associated with HF, early detection is becoming increasingly critical. Many studies have focused on the field of heart disease diagnosis based on heart [...] Read more.
Heart failure (HF) is a devastating condition that impairs people’s lives and health. Because of the high morbidity and mortality associated with HF, early detection is becoming increasingly critical. Many studies have focused on the field of heart disease diagnosis based on heart sound (HS), demonstrating the feasibility of sound signals in heart disease diagnosis. In this paper, we propose a non-invasive early diagnosis method for HF based on a deep learning (DL) network and the Korotkoff sound (KS). The accuracy of the KS-based HF prediagnosis was investigated utilizing continuous wavelet transform (CWT) features, Mel frequency cepstrum coefficient (MFCC) features, and signal segmentation. Fivefold cross-validation was applied to the four DL models: AlexNet, VGG19, ResNet50, and Xception, and the performance of each model was evaluated using accuracy (Acc), specificity (Sp), sensitivity (Se), area under curve (AUC), and time consumption (Tc). The results reveal that the performance of the four models on MFCC datasets is significantly improved when compared to CWT datasets, and each model performed considerably better on the non-segmented dataset than on the segmented dataset, indicating that KS signal segmentation and feature extraction had a significant impact on the KS-based CHF prediagnosis performance. Our method eventually achieves the prediagnosis results of Acc (96.0%), Se (97.5%), and Sp (93.8%) based on a comparative study of the model and the data set. The research demonstrates that the KS-based prediagnosis method proposed in this paper could accomplish accurate HF prediagnosis, which will offer new research approaches and a more convenient way to achieve early HF prevention. Full article
(This article belongs to the Special Issue Advanced Medical Signal Processing and Visualization)
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