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Applied Sciences
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Image Processing in Biomedical Engineering—Latest Advances and Prospects
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Image Processing in Biomedical Engineering—Latest Advances and Prospects

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 February 2024) | Viewed by 9737

Special Issue Editor

Dr. Manuel G. Forero

E-Mail Website
Guest Editor
Faculty of Engineering, University of Ibagué, Ibagué, Colombia
Interests: digital image processing; microscopy imaging; machine learning; biomedical imaging

Special Issue Information

Dear Colleagues,

This Special Issue on biomedical image processing is pleased to invite researchers to submit original research articles for publication.

Image processing has become a fundamental tool in the analysis of medical samples. From microscopy imaging to tomography and a number of other important techniques, image processing is used to identify patterns and abnormalities in medical specimens, and is essential for the diagnosis and treatment of diseases.

Medical and microscopy images are vital tools for the analysis and diagnosis of a wide range of diseases and conditions. They provide a detailed view of a patient’s condition, and help clinicians and researchers to identify patterns, abnormalities and other critical features that can be used to make a more accurate diagnosis or assess the efficacy of a treatment. Advances in imaging technology have greatly improved the ability to acquire and analyze medical and microscopic images, and have enabled scientists to understand complex biological processes at the cellular and molecular level. As a result, the use of medical and microscopic imaging has become an essential part of medical research, enabling clinicians and researchers to identify and explore the underlying causes of disease, test new treatments and develop more effective interventions to improve patient outcomes.

Animal models have long been an important tool for studying diseases and developing new treatments, allowing researchers to explore the underlying causes of disease and test new therapies in a controlled environment. In many cases, these models using microscopy and other medical imaging can provide essential information about human diseases, allowing researchers to develop new treatments and interventions that can improve patient outcomes.

In recent years, there has been growing interest in the use of biomedical signal processing to complement and enhance the information obtained from medical imaging. Biomedical signal processing techniques are used to analyze and interpret signals obtained from physiological systems, such as ECG, EEG and EMG signals. These signals can be used to better understand how the body functions and how diseases affect it. By integrating the analysis of biomedical signals with that of medical imaging, researchers can gain a more complete understanding of a patient’s condition, which can lead to more accurate diagnoses and more effective treatments.

In this Special Issue, we are interested in receiving articles on any topic related to biomedical image and biomedical signal processing, including, but not limited to, medical image filtering, analysis, segmentation and registration, biomedical signal processing, computer-aided diagnosis and applications of biomedical imaging in cell biology, biochemistry, pharmacology and other related fields using methods based on either classical image processing or machine learning.

Dr. Manuel G. Forero
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.

Published Papers (4 papers)

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Research

19 pages, 6303 KiB  
Article
Automated Brain Tumor Identification in Biomedical Radiology Images: A Multi-Model Ensemble Deep Learning Approach
by Sarfaraz Natha, Umme Laila, Ibrahim Ahmed Gashim, Khalid Mahboob, Muhammad Noman Saeed and Khaled Mohammed Noaman
Appl. Sci. 2024, 14(5), 2210; https://doi.org/10.3390/app14052210 - 6 Mar 2024
Viewed by 1052
Abstract
Brain tumors (BT) represent a severe and potentially life-threatening cancer. Failing to promptly diagnose these tumors can significantly shorten a person’s life. Therefore, early and accurate detection of brain tumors is essential, allowing for appropriate treatment and improving the chances of a patient’s [...] Read more.
Brain tumors (BT) represent a severe and potentially life-threatening cancer. Failing to promptly diagnose these tumors can significantly shorten a person’s life. Therefore, early and accurate detection of brain tumors is essential, allowing for appropriate treatment and improving the chances of a patient’s survival. Due to the different characteristics and data limitations of brain tumors is challenging problems to classify the three different types of brain tumors. A convolutional neural networks (CNNs) learning algorithm integrated with data augmentation techniques was used to improve the model performance. CNNs have been extensively utilized in identifying brain tumors through the analysis of Magnetic Resonance Imaging (MRI) images The primary aim of this research is to propose a novel method that achieves exceptionally high accuracy in classifying the three distinct types of brain tumors. This paper proposed a novel Stack Ensemble Transfer Learning model called “SETL_BMRI”, which can recognize brain tumors in MRI images with elevated accuracy. The SETL_BMRI model incorporates two pre-trained models, AlexNet and VGG19, to improve its ability to generalize. Stacking combined outputs from these models significantly improved the accuracy of brain tumor detection as compared to individual models. The model’s effectiveness is evaluated using a public brain MRI dataset available on Kaggle, containing images of three types of brain tumors (meningioma, glioma, and pituitary). The experimental findings showcase the robustness of the SETL_BMRI model, achieving an overall classification accuracy of 98.70%. Additionally, it delivers an average precision, recall, and F1-score of 98.75%, 98.6%, and 98.75%, respectively. The evaluation metric values of the proposed solution indicate that it effectively contributed to previous research in terms of achieving high detection accuracy. Full article
(This article belongs to the Special Issue Image Processing in Biomedical Engineering—Latest Advances and Prospects)
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25 pages, 5175 KiB  
Article
Adaptive Smoothing for Visual Improvement of Image Quality via the p(x)-Laplacian Operator Effects of the p(x)-Laplacian Smoothing Operator on Digital Image Restoration: Contribution to an Adaptive Control Criterion
by Jean-Luc Henry, Jimmy Nagau, Jean Velin and Issa-Paul Moussa
Appl. Sci. 2023, 13(20), 11600; https://doi.org/10.3390/app132011600 - 23 Oct 2023
Viewed by 727
Abstract
This article concerns the improvement of digital image quality using mathematical tools such as nonlinear partial differential operators. In this paper, to perform smoothing on digital images, we propose to use the p(x)-Laplacian operator. Its smoothing power plays a main role in the [...] Read more.
This article concerns the improvement of digital image quality using mathematical tools such as nonlinear partial differential operators. In this paper, to perform smoothing on digital images, we propose to use the p(x)-Laplacian operator. Its smoothing power plays a main role in the restoration process. This enables us to dynamically process certain areas of an image. We used a mathematical model of image regularisation that is described by a nonlinear diffusion Equation (this diffusion is modelled by the p(x)-Laplacian operator). We implemented the continuous model in order to observe the steps of the regularisation process to understand the effects of the different parameters of the model on an image. This will enable parameters to be used and adapted in order to provide a proposed solution. Full article
(This article belongs to the Special Issue Image Processing in Biomedical Engineering—Latest Advances and Prospects)
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17 pages, 2333 KiB  
Article
Object Detection for Brain Cancer Detection and Localization
by Francesco Mercaldo, Luca Brunese, Fabio Martinelli, Antonella Santone and Mario Cesarelli
Appl. Sci. 2023, 13(16), 9158; https://doi.org/10.3390/app13169158 - 11 Aug 2023
Cited by 2 | Viewed by 2909
Abstract
Brain cancer is acknowledged as one of the most aggressive tumors, with a significant impact on patient survival rates. Unfortunately, approximately 70% of patients diagnosed with this malignant cancer do not survive. This paper introduces a method designed to detect and localize brain [...] Read more.
Brain cancer is acknowledged as one of the most aggressive tumors, with a significant impact on patient survival rates. Unfortunately, approximately 70% of patients diagnosed with this malignant cancer do not survive. This paper introduces a method designed to detect and localize brain cancer by proposing an automated approach for the detection and localization of brain cancer. The method utilizes magnetic resonance imaging analysis. By leveraging the information provided by brain medical images, the proposed method aims to enhance the detection and precise localization of brain cancer to improve the prognosis and treatment outcomes for patients. We exploit the YOLO model to automatically detect and localize brain cancer: in the analysis of 300 brain images we obtain a precision of 0.943 and a recall of 0.923 in brain cancer detection while, relating to brain cancer localization, an mAP_0.5 equal to 0.941 is reached, thus showing the effectiveness of the proposed model for brain cancer detection and localization. Full article
(This article belongs to the Special Issue Image Processing in Biomedical Engineering—Latest Advances and Prospects)
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18 pages, 10605 KiB  
Article
BUU-LSPINE: A Thai Open Lumbar Spine Dataset for Spondylolisthesis Detection
by Podchara Klinwichit, Watcharaphong Yookwan, Sornsupha Limchareon, Krisana Chinnasarn, Jun-Su Jang and Athita Onuean
Appl. Sci. 2023, 13(15), 8646; https://doi.org/10.3390/app13158646 - 27 Jul 2023
Cited by 3 | Viewed by 4372
Abstract
(1) Background: Spondylolisthesis, a common disease among older individuals, involves the displacement of vertebrae. The condition may gradually manifest with age, allowing for potential prevention by the research of predictive algorithms. However, one key issue that hinders research in spondylolisthesis prediction algorithms is [...] Read more.
(1) Background: Spondylolisthesis, a common disease among older individuals, involves the displacement of vertebrae. The condition may gradually manifest with age, allowing for potential prevention by the research of predictive algorithms. However, one key issue that hinders research in spondylolisthesis prediction algorithms is the need for publicly available spondylolisthesis datasets. (2) Purpose: This paper introduces BUU-LSPINE, a new dataset for the lumbar spine. It includes 3600 patients’ plain film images annotated with vertebral position, spondylolisthesis diagnosis, and lumbosacral transitional vertebrae (LSTV) ground truth. (4) Methods: We established an annotation pipeline to create the BUU-SPINE dataset and evaluated it in three experiments as follows: (1) lumbar vertebrae detection, (2) vertebral corner points extraction, and (3) spondylolisthesis prediction. (5) Results: Lumbar vertebrae detection achieved the highest precision rates of 81.93% on the AP view and 83.45% on the LA view using YOLOv5; vertebral corner point extraction achieved the lowest average error distance of 4.63 mm on the AP view using ResNet152V2 and 4.91 mm on the LA view using DenseNet201. Spondylolisthesis prediction reached the highest accuracy of 95.14% on the AP view and 92.26% on the LA view of a testing set using Support Vector Machine (SVM). (6) Discussions: The results of the three experiments highlight the potential of BUU-LSPINE in developing and evaluating algorithms for lumbar vertebrae detection and spondylolisthesis prediction. These steps are crucial in advancing the creation of a clinical decision support system (CDSS). Additionally, the findings demonstrate the impact of Lumbosacral transitional vertebrae (LSTV) conditions on lumbar detection algorithms. Full article
(This article belongs to the Special Issue Image Processing in Biomedical Engineering—Latest Advances and Prospects)
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