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Biosignal and Medical Image Processing
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Biosignal and Medical Image Processing

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Biomedical Information and Health".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 21333

Special Issue Editors

Dr. Simone Palazzo

E-Mail Website
Guest Editor
Department of Electrical, Electronic and Computer Engineering, PeRCeiVe Lab, University of Catania, Catania, Italy
Interests: computer vision; medical image analysis; video understanding
Special Issues, Collections and Topics in MDPI journals
Dr. Carmelo Pino

E-Mail Website
Guest Editor
Department of Electrical, Electronic and Computer Engineering, University of Catania, Catania, Italy
Interests: computer vision; medical image analysis

Special Issue Information

Dear Colleagues,

The success of recent machine learning approaches in a variety of research areas has pushed automated data analysis towards human (if not superhuman) performance in several tasks. However, some domain-specific problems may present certain difficulties that current deep learning approaches are not able to fully overcome.

Among these problems, medical data analysis is probably the one for which most interest and efforts are dedicated, especially in these times of health crisis due to the COVID-19 pandemic. Indeed, while it is possible to apply learning techniques that work well on general-purpose applications as well as with medical data, medical data analysis is particularly complex, for several reasons.

First and foremost, the lack of large-scale annotated datasets, which has proven to be a key factor for domain-generic tasks, is a main hindrance to the development of tools that can successfully and reliably support physicians in their jobs. As an additional difficulty, dataset fusion is made more complex by the huge variability introduced by measuring/scanning devices, environmental conditions of exams, and population-specific factors, making generalizable models hard to train.

Moreover, while a lot of research on medical data analysis has focused on medical image analysis, which is naturally supported by the vast literature on computer vision, there are several data modalities that can be equally important but have not attracted as much interest by scientists, e.g., EEG and ECG.

Finally, while many existing techniques target the problem of disease diagnosis using supervised approaches, few works tackle the problem of knowledge discovery from medical data, in order to understand more about the way certain physiological processes work—the inner working of our brain is the most evident example.

This Special Issue has the objective of collecting novel and impactful works on biosignal and medical image processing, including but not limited to the following topics:

  • Medical image analysis;
  • Medical video analysis;
  • Medical biosignal analysis;
  • Dataset collection for medicine;
  • Medical dataset fusion and integration;
  • Multi-modal approaches for medicine;
  • Transfer learning approaches for medicine;
  • Continual learning in medicine;
  • Diagnosis approaches for known diseases;
  • Unsupervised approaches for knowledge discovery in medicine.

Dr. Simone Palazzo
Dr. Carmelo Pino
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. Information is an international peer-reviewed open access monthly 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 1600 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

  • medical image analysis
  • biosignal analysis
  • machine learning for medicine
  • dataset collection and integration for medicine
  • computer-aided diagnosis
  • artificial intelligence for medicine
  • computer vision for medicine

Published Papers (6 papers)

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Research

17 pages, 771 KiB  
Article
Deep-Learning-Based Human Chromosome Classification: Data Augmentation and Ensemble
by Mattia D’Angelo and Loris Nanni
Information 2023, 14(7), 389; https://doi.org/10.3390/info14070389 - 9 Jul 2023
Cited by 2 | Viewed by 2610
Abstract
Object classification is a crucial task in deep learning, which involves the identification and categorization of objects in images or videos. Although humans can easily recognize common objects, such as cars, animals, or plants, performing this task on a large scale can be [...] Read more.
Object classification is a crucial task in deep learning, which involves the identification and categorization of objects in images or videos. Although humans can easily recognize common objects, such as cars, animals, or plants, performing this task on a large scale can be time-consuming and error-prone. Therefore, automating this process using neural networks can save time and effort while achieving higher accuracy. Our study focuses on the classification step of human chromosome karyotyping, an important medical procedure that helps diagnose genetic disorders. Traditionally, this task is performed manually by expert cytologists, which is a time-consuming process that requires specialized medical skills. Therefore, automating it through deep learning can be immensely useful. To accomplish this, we implemented and adapted existing preprocessing and data augmentation techniques to prepare the chromosome images for classification. We used ResNet-50 convolutional neural network and Swin Transformer, coupled with an ensemble approach to classify the chromosomes, obtaining state-of-the-art performance in the tested dataset. Full article
(This article belongs to the Special Issue Biosignal and Medical Image Processing)
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20 pages, 8569 KiB  
Article
Subject-Independent per Beat PPG to Single-Lead ECG Mapping
by Khaled M. Abdelgaber, Mostafa Salah, Osama A. Omer, Ahmed E. A. Farghal and Ahmed S. Mubarak
Information 2023, 14(7), 377; https://doi.org/10.3390/info14070377 - 3 Jul 2023
Cited by 3 | Viewed by 1706
Abstract
In this paper, a beat-based autoencoder is proposed for mapping photoplethysmography (PPG) to a single-lead electrocardiogram (single-lead ECG) signal. The main limiting factors represented in uncleaned data, subject dependency, and erroneous beat segmentation are regarded. The dataset is cleaned by a two-stage clustering [...] Read more.
In this paper, a beat-based autoencoder is proposed for mapping photoplethysmography (PPG) to a single-lead electrocardiogram (single-lead ECG) signal. The main limiting factors represented in uncleaned data, subject dependency, and erroneous beat segmentation are regarded. The dataset is cleaned by a two-stage clustering approach. Rather than complete single–lead ECG signal reconstruction, a beat-based PPG-to-single-lead-ECG (PPG2ECG) conversion is introduced for providing a simple lightweight model that meets the computational capabilities of wearable devices. In addition, peak-to-peak segmentation is employed for alleviating errors in PPG onset detection. Furthermore, subject-dependent training is highlighted as a critical factor in training procedures because most existing work includes different beats/signals from the same subject’s record in both training and testing sets. So, we provide a completely subject-independent model where the testing subjects’ records are hidden in the training stage entirely, i.e., a subject record appears once either in the training or testing set, but testing beats/signals belong to records that never appear in the training set. The proposed deep learning model is designed for providing efficient feature extraction that attains high reconstruction quality over subject-independent scenarios. The achieved performance is about 0.92 for the correlation coefficient and 0.0086 for the mean square error for the dataset extracted/cleaned from the MIMIC II dataset. Full article
(This article belongs to the Special Issue Biosignal and Medical Image Processing)
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18 pages, 3448 KiB  
Article
Nonlinear Activation-Free Contextual Attention Network for Polyp Segmentation
by Weidong Wu, Hongbo Fan, Yu Fan and Jian Wen
Information 2023, 14(7), 362; https://doi.org/10.3390/info14070362 - 26 Jun 2023
Viewed by 1018
Abstract
The accurate segmentation of colorectal polyps is of great significance for the diagnosis and treatment of colorectal cancer. However, the segmentation of colorectal polyps faces complex problems such as low contrast in the peripheral region of salient images, blurred borders, and diverse shapes. [...] Read more.
The accurate segmentation of colorectal polyps is of great significance for the diagnosis and treatment of colorectal cancer. However, the segmentation of colorectal polyps faces complex problems such as low contrast in the peripheral region of salient images, blurred borders, and diverse shapes. In addition, the number of traditional UNet network parameters is large and the segmentation effect is average. To overcome these problems, an innovative nonlinear activation-free uncertainty contextual attention network is proposed in this paper. Based on the UNet network, an encoder and a decoder are added to predict the saliency map of each module in the bottom-up flow and pass it to the next module. We use Res2Net as the backbone network to extract image features, enhance image features through simple parallel axial channel attention, and obtain high-level features with global semantics and low-level features with edge details. At the same time, a nonlinear n on-activation network is introduced, which can reduce the complexity between blocks, thereby further enhancing image feature extraction. This work conducted experiments on five commonly used polyp segmentation datasets, and the experimental evaluation metrics from the mean intersection over union, mean Dice coefficient, and mean absolute error were all improved, which can show that our method has certain advantages over existing methods in terms of segmentation performance and generalization performance. Full article
(This article belongs to the Special Issue Biosignal and Medical Image Processing)
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16 pages, 4690 KiB  
Article
3D Reconstruction with Coronary Artery Based on Curve Descriptor and Projection Geometry-Constrained Vasculature Matching
by Jijun Tong, Shuai Xu, Fangliang Wang and Pengjia Qi
Information 2022, 13(1), 38; https://doi.org/10.3390/info13010038 - 13 Jan 2022
Viewed by 2218
Abstract
This paper presents a novel method based on a curve descriptor and projection geometry constrained for vessel matching. First, an LM (Leveberg–Marquardt) algorithm is proposed to optimize the matrix of geometric transformation. Combining with parameter adjusting and the trust region method, the error [...] Read more.
This paper presents a novel method based on a curve descriptor and projection geometry constrained for vessel matching. First, an LM (Leveberg–Marquardt) algorithm is proposed to optimize the matrix of geometric transformation. Combining with parameter adjusting and the trust region method, the error between 3D reconstructed vessel projection and the actual vessel can be minimized. Then, CBOCD (curvature and brightness order curve descriptor) is proposed to indicate the degree of the self-occlusion of blood vessels during angiography. Next, the error matrix constructed from the error of epipolar matching is used in point pairs matching of the vascular through dynamic programming. Finally, the recorded radius of vessels helps to construct ellipse cross-sections and samples on it to get a point set around the centerline and the point set is converted to mesh for reconstructing the surface of vessels. The validity and applicability of the proposed methods have been verified through experiments that result in the significant improvement of 3D reconstruction accuracy in terms of average back-projection errors. Simultaneously, due to precise point-pair matching, the smoothness of the reconstructed 3D coronary artery is guaranteed. Full article
(This article belongs to the Special Issue Biosignal and Medical Image Processing)
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11 pages, 2095 KiB  
Article
Tortuosity Index Calculations in Retinal Images: Some Criticalities Arising from Commonly Used Approaches
by Francesco Martelli and Claudia Giacomozzi
Information 2021, 12(11), 466; https://doi.org/10.3390/info12110466 - 10 Nov 2021
Cited by 4 | Viewed by 10954
Abstract
A growing body of research in retinal imaging is recently considering vascular tortuosity measures or indexes, with definitions and methods mostly derived from cardiovascular research. However, retinal microvasculature has its own peculiarities that must be considered in order to produce reliable measurements. This [...] Read more.
A growing body of research in retinal imaging is recently considering vascular tortuosity measures or indexes, with definitions and methods mostly derived from cardiovascular research. However, retinal microvasculature has its own peculiarities that must be considered in order to produce reliable measurements. This study analyzed and compared various derived metrics (e.g., TI, TI_avg, TI*CV) across four existing computational workflows. Specifically, the implementation of the models on two critical OCT images highlighted main pitfalls of the methods, which may fail in reliably differentiating a highly tortuous image from a normal one. A tentative, encouraging approach to mitigate the issue on the same OCT exemplificative images is described in the paper, based on the suggested index TI*CV. Full article
(This article belongs to the Special Issue Biosignal and Medical Image Processing)
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12 pages, 3348 KiB  
Article
Biological Tissue Damage Monitoring Method Based on IMWPE and PNN during HIFU Treatment
by Bei Liu, Xian Zhang, Xiao Zou, Jing Cao and Ziqi Peng
Information 2021, 12(10), 404; https://doi.org/10.3390/info12100404 - 30 Sep 2021
Cited by 4 | Viewed by 1581
Abstract
Biological tissue damage monitoring is an indispensable part of high-intensity focused ultrasound (HIFU) treatment. As a nonlinear method, multi-scale permutation entropy (MPE) is widely used in the monitoring of biological tissue. However, the traditional MPE method neglects the amplitude information when calculating the [...] Read more.
Biological tissue damage monitoring is an indispensable part of high-intensity focused ultrasound (HIFU) treatment. As a nonlinear method, multi-scale permutation entropy (MPE) is widely used in the monitoring of biological tissue. However, the traditional MPE method neglects the amplitude information when calculating the time series complexity, and the stability of MPE is poor due to the defects in the coarse-grained process. In order to solve the above problems, the method of improved coarse-grained multi-scale weighted permutation entropy (IMWPE) is proposed in this paper. Compared with the MPE, the IMWPE method not only includes the amplitude of signal when calculating the signal complexity, but also improves the stability of entropy value. The IMWPE method is applied to the HIFU echo signals during HIFU treatment, and the probabilistic neural network (PNN) is used for monitoring the biological tissue damage. The results show that compared with multi-scale sample entropy (MSE)-PNN and MPE-PNN methods, the proposed IMWPE-PNN method can correctly identify all the normal tissues, and can more effectively identify damaged tissues and denatured tissues. The recognition rate for the three kinds of biological tissues is higher, up to 96.7%. This means that the IMWPE-PNN method can better monitor the status of biological tissue damage during HIFU treatment. Full article
(This article belongs to the Special Issue Biosignal and Medical Image Processing)
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