Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Biomedical Sensing System Based on Image Analysis

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

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

Deadline for manuscript submissions: 25 June 2025 | Viewed by 6361

Share This Special Issue

Special Issue Editors

Dr. Chen Li

E-Mail Website
Guest Editor

College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
Interests: microscopic image and medical image analysis; artificial intelligence; pattern recognition; machine learning; machine vision; multimedia retrieval; intelligent microscopic imaging technology
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Marcin Grzegorzek

E-Mail Website
Guest Editor

Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
Interests: biomedical engineering; artificial intelligence; pattern recognition; machine vision; machine learning; medical sensor
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Medical images are one of the most important sources of biomedical sensing data, including radiological images, pathological images and photographs of affected areas with special instruments. The sensor system developed based on medical images can not only integrate multi-modal medical data information to realize real-time monitoring of patients' conditions, but also predict patients' prognosis and even rehabilitation. In addition, biomedical sensing systems based on medical images offer the possibility of a standardized disease assessment that removes subjective judgments. In particular, some new techniques are introduced in this domain, such as Medical Image Processing Knowledge Editing for LLM Aircraft Detection and Recognition with Few-Shot Medical Image Segmentation based on domain adaption multi-omics information integration and Infrared Small Target Detection. Finally, we welcome the submission of manuscripts to our Special Issue on (but not limited to) the following topics:

Test and analysis of the application effect of medical image key point detection algorithms;
Test and analysis of the application effect of medical image segmentation algorithms;
Test and analysis of the application effect of medical image target classification algorithms;
The application of artificial intelligence in this digital measurement of human anatomy;
Application of artificial intelligence in the diagnosis of coronary heart disease;
Application of artificial intelligence in the diagnosis of pulmonary nodules;
Application of human intelligence in spinal measurement;
Calculation of the risk of disease or other unexpected events for healthy people or patients based on medical images;
Use of real-time medical images to detect disease in patients, so as to assist treatment or provide an early warning of changes in patients' conditions;
Perform diagnosis and differential diagnosis through medical images during the course of the disease, assist in the formulation of medical plans and predict the prognosis of patients;
Based on medical images, evaluate the information that is difficult to obtain manually on images, including molecular biological characteristics and metabolites;
Adopt new methods, semi-automatic or fully automatic medical information extraction, so as to improve efficiency.

Dr. Chen Li
Prof. Dr. Marcin Grzegorzek
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

medical image analysis
image classification
image segmentation
object detection
feature extraction

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

25 pages, 5733 KiB

Open AccessArticle

Comparative Analysis of Edge Detection Operators Using a Threshold Estimation Approach on Medical Noisy Images with Different Complexities

by Vladimir Maksimovic, Branimir Jaksic, Mirko Milosevic, Jelena Todorovic and Lazar Mosurovic

Sensors 2025, 25(1), 87; https://doi.org/10.3390/s25010087 - 27 Dec 2024

Cited by 2 | Viewed by 1156

Abstract

The manuscript conducts a comparative analysis to assess the impact of noise on medical images using a proposed threshold value estimation approach. It applies an innovative method for edge detection on images of varying complexity, considering different noise types and concentrations of noise. Five edges are evaluated on images with low, medium, and high detail levels. This study focuses on medical images from three distinct datasets: retinal images, brain tumor segmentation, and lung segmentation from CT scans. The importance of noise analysis is heightened in medical imaging, as noise can significantly obscure the critical features and potentially lead to misdiagnoses. Images are categorized based on the complexity, providing a multidimensional view of noise’s effect on edge detection. The algorithm utilized the grid search (GS) method and random search with nine values (RS9). The results demonstrate the effectiveness of the proposed approach, especially when using the Canny operator, across diverse noise types and intensities. Laplace operators are most affected by noise, yet significant improvements are observed with the new approach, particularly when using the grid search method. The obtained results are compared with the most popular techniques for edge detection using deep learning like AlexNet, ResNet, VGGNet, MobileNetv2, and Inceptionv3. The paper presents the results via graphs and edge images, along with a detailed analysis of each operator’s performance with noisy images using the proposed approach. Full article

(This article belongs to the Special Issue Biomedical Sensing System Based on Image Analysis)

► Show Figures

Figure 1

13 pages, 11191 KiB

Open AccessArticle

The Adaption of Recent New Concepts in Neural Radiance Fields and Their Role for High-Fidelity Volume Reconstruction in Medical Images

by Haill An, Jawad Khan, Suhyeon Kim, Junseo Choi and Younhyun Jung

Sensors 2024, 24(18), 5923; https://doi.org/10.3390/s24185923 - 12 Sep 2024

Cited by 1 | Viewed by 1995

Abstract

Volume reconstruction techniques are gaining increasing interest in medical domains due to their potential to learn complex 3D structural information from sparse 2D images. Recently, neural radiance fields (NeRF), which implicitly model continuous radiance fields based on multi-layer perceptrons to enable volume reconstruction of objects at arbitrary resolution, have gained traction in natural image volume reconstruction. However, the direct application of NeRF to medical volume reconstruction presents unique challenges due to differences in imaging principles, internal structure requirements, and boundary delineation. In this paper, we evaluate different NeRF techniques developed for natural images, including sampling strategies, feature encoding, and the use of complimentary features, by applying them to medical images. We evaluate three state-of-the-art NeRF techniques on four datasets of medical images of different complexity. Our goal is to identify the strengths, limitations, and future directions for integrating NeRF into the medical domain. Full article

(This article belongs to the Special Issue Biomedical Sensing System Based on Image Analysis)

► Show Figures

Figure 1

10 pages, 1699 KiB

Open AccessArticle

Ultrashort-Echo-Time MRI of the Disco-Vertebral Junction: Modulation of Image Contrast via Echo Subtraction and Echo Times

by Karen C. Chen, Palanan Siriwananrangsun and Won C. Bae

Sensors 2024, 24(17), 5842; https://doi.org/10.3390/s24175842 - 9 Sep 2024

Cited by 1 | Viewed by 1493

Abstract

Introduction: The disco-vertebral junction (DVJ) of the lumbar spine contains thin structures with short T2 values, including the cartilaginous endplate (CEP) sandwiched between the bony vertebral endplate (VEP) and the nucleus pulposus (NP). We previously demonstrated that ultrashort-echo-time (UTE) MRI, compared to conventional MRI, is able to depict the tissues at the DVJ with improved contrast. In this study, we sought to further optimize UTE MRI by characterizing the contrast-to-noise ratio (CNR) of these tissues when either single echo or echo subtraction images are used and with varying echo times (TEs). Methods: In four cadaveric lumbar spines, we acquired 3D Cones (a UTE sequence) images at varying TEs from 0.032 ms to 16 ms. Additionally, spin echo T1- and T2-weighted images were acquired. The CNRs of CEP-NP and CEP-VEP were measured in all source images and 3D Cones echo subtraction images. Results: In the spin echo images, it was challenging to distinguish the CEP from the VEP, as both had low signal intensity. However, the 3D Cones source images at the shortest TE of 0.032 ms provided an excellent contrast between the CEP and the VEP. As the TE increased, the contrast decreased in the source images. In contrast, the 3D Cones echo subtraction images showed increasing CNR values as the second TE increased, reaching statistical significance when the second TE was above 10 ms (p < 0.05). Conclusions: Our study highlights the feasibility of incorporating UTE MRI for the evaluation of the DVJ and its advantages over conventional spin echo sequences for improving the contrast between the CEP and adjacent tissues. Additionally, modulation of the contrast for the target tissues can be achieved using either source images or subtraction images, as well as by varying the echo times. Full article

(This article belongs to the Special Issue Biomedical Sensing System Based on Image Analysis)

► Show Figures

Figure 1

14 pages, 4262 KiB

Open AccessArticle

DEIT-Based Bone Position and Orientation Estimation for Robotic Support in Total Knee Arthroplasty—A Computational Feasibility Study

by Jakob Schrott, Sabrina Affortunati, Christian Stadler and Christoph Hintermüller

Sensors 2024, 24(16), 5269; https://doi.org/10.3390/s24165269 - 14 Aug 2024

Cited by 1 | Viewed by 959

Abstract

Total knee arthroplasty (TKA) is a well-established and successful treatment option for patients with end-stage osteoarthritis of the knee, providing high patient satisfaction. Robotic systems have been widely adopted to perform TKA in orthopaedic centres. The exact spatial positions of the femur and tibia are usually determined through pinned trackers, providing the surgeon with an exact illustration of the axis of the lower limb. The drilling of holes required for mounting the trackers creates weak spots, causing adverse events such as bone fracture. In the presented computational feasibility study, time differential electrical impedance tomography is used to locate the femur positions, thereby the difference in conductivity distribution between two distinct states

s_{0}

and

s_{1}

of the measured object is reconstructed. The overall approach was tested by simulating five different configurations of thigh shape and considered tissue conductivity distributions. For the cylinder models used for verification and reference, the reconstructed position deviated by about

\approx 1

mm from the actual bone centre. In case of models mimicking a realistic cross section of the femur position deviated between 7.9 mm 24.8 mm. For all models, the bone axis was off by about

φ = 1.50 °

from its actual position. Full article

(This article belongs to the Special Issue Biomedical Sensing System Based on Image Analysis)

► Show Figures

Journal Menu

Journal Browser

Biomedical Sensing System Based on Image Analysis

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (4 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI