Advances in Signal and Image Processing for Biomedical Applications

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

Deadline for manuscript submissions: 30 June 2024 | Viewed by 6299

Special Issue Editors

School of Automation, University of Electronic Science and Technology of China, Chengdu, China
Interests: computer vision; surgical robots; medical image processing
Special Issues, Collections and Topics in MDPI journals
School of Computer Science and Technology, East China Normal University, Shanghai 200062, China
Interests: multi-agent reinforcement learning and applications
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
College of Electronic Information and Automation, Tianjin University of Science and Technology, Tianjin 300000, China
Interests: medical image processing; pattern recognition; electrical tomography

Special Issue Information

Dear Colleagues,

This Special Issue aims to highlight recent advances in signal and image processing techniques in medical applications. Over the past decade, deep learning techniques have revolutionized the processing of signals, images, and videos. Deep-learning-based algorithms have achieved great success, especially in natural language processing and tasks related to image and vision, such as classification, recognition, detection, segmentation, and reconstruction. The progress in deep learning technology is largely driven by the improvement of computing power (especially parallel computing power) and the convenience of building large-scale training data sets with the Internet. However, in the field of medical applications, deep learning still faces many challenges, mainly including: 1) the cost of medical data collection and labeling is high; 2) the data volume of medical data (e.g., high-resolution CT) is large, which usually requires more efficient processing algorithms; and 3) the black-box nature of deep learning methods leads to their lack of interpretability in medical tasks, and it is difficult to gain the trust of doctors, regulators and patients.

This Special Issue welcomes all recent research works in signal and image processing for applications in medicine, especially those that fuse traditional and deep learning techniques, unsupervised or self-supervised methods, and interpretable deep learning models built for medical purposes. Potential topics in this collection include, but are not limited to, the following topics:

  • Medical image (e.g., CT, MRI, ultrasound) processing
  • Surgical vision (applications of computer vision in surgery)
  • Medical signal processing
  • Medical image reconstruction
  • Medical image classification, detection, localization and segmentation
  • Intelligent diagnostic system based on medical signals, image and data

Dr. Bo Yang
Dr. Bo Jin
Prof. Dr. Xiaoyan Chen
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 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

22 pages, 5027 KiB  
Article
Reconstructing Nerve Structures from Unorganized Points
by Jelena Kljajić, Goran Kvaščev and Željko Đurović
Appl. Sci. 2023, 13(20), 11421; https://doi.org/10.3390/app132011421 - 18 Oct 2023
Viewed by 650
Abstract
Realistic sensory feedback is paramount for amputees as it improves prosthetic limb control and boosts functionality, safety, and overall quality of life. This sensory restoration relies on the direct electrostimulation of residual peripheral nerves. Computational models are instrumental in simulating these neurostimulation effects, [...] Read more.
Realistic sensory feedback is paramount for amputees as it improves prosthetic limb control and boosts functionality, safety, and overall quality of life. This sensory restoration relies on the direct electrostimulation of residual peripheral nerves. Computational models are instrumental in simulating these neurostimulation effects, offering solutions to the complexities tied to extensive animal/human trials and costly materials. Central to these models is the detailed mapping of nerve geometry, necessitating the delineation of internal nerve structures, such as fascicles, across various cross-sections. In our modeling process, we faced the challenge of organizing an originally unstructured set of points into coherent contours. We introduced a parameter-free curve-reconstruction algorithm that combines valley-seeking clustering, an adaptive Kalman filter, and the nearest neighbor classification technique. While intuitively simple for humans, the task of reconstructing multiple open and/or closed lines with pronounced corners from a nonuniform point set is daunting for many algorithms. Additionally, the precise differentiation of adjacent curves, commonly encountered in realistic nerve models, remains a formidable challenge even for top-tier algorithms. Our proposed method adeptly navigates the complexities inherent to nerve structure reconstruction. While our algorithm is chiefly designed for closed curves, as dictated by nerve geometry, we believe it can be reconfigured with appropriate code adjustments to handle open curves. Beyond neuroprosthetics, our proposed model has the potential to be applied and spark innovations in biomedicine and a variety of other fields. Full article
(This article belongs to the Special Issue Advances in Signal and Image Processing for Biomedical Applications)
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14 pages, 3150 KiB  
Article
Left Ventricular Ejection Time Estimation from Blood Pressure and Photoplethysmography Signals Based on Tidal Wave
by Lucian Evdochim, Dragoș Dobrescu, Lidia Dobrescu, Silviu Stanciu and Stela Halichidis
Appl. Sci. 2023, 13(19), 11025; https://doi.org/10.3390/app131911025 - 6 Oct 2023
Viewed by 1450
Abstract
Left ventricular ejection time (LVET) is an important parameter for assessing cardiovascular disorders. In a medical office, it is typically measured using the Tissue Doppler Imaging technique, but new wearable devices have led to a growing interest in integrating this parameter into them, [...] Read more.
Left ventricular ejection time (LVET) is an important parameter for assessing cardiovascular disorders. In a medical office, it is typically measured using the Tissue Doppler Imaging technique, but new wearable devices have led to a growing interest in integrating this parameter into them, increasing accessibility to personalized healthcare for users and patients. In the cardiovascular domain, photoplethysmography (PPG) is a promising technology that shares two distinctive features with invasive arterial blood pressure (ABP) tracing: the tidal wave (TDW) and the dicrotic wave (DCW). In the early years of cardiovascular research, the duration of the dicrotic point was initially linked to the ending phase of left ventricular ejection. Subsequent studies reported deviations from the initial association, suggesting that the ejection period is related to the tidal wave feature. In this current study, we measured left ventricular ejection time in both ABP and PPG waveforms, considering recent research results. A total of 27,000 cardiac cycles were analyzed for both afore-mentioned signals. The reference value for ejection time was computed based on the T-wave segment duration from the electrocardiogram waveform. In lower blood pressure, which is associated with decreased heart contractility, the results indicated an underestimation of −29 ± 19 ms in ABP and an overestimation of 18 ± 31 ms in PPG. On the other side of the spectrum, during increased contractility, the minimum errors were −3 ± 18 ms and 4 ± 33 ms, respectively. Since the tidal wave feature is strongly affected by arterial tree compliance, the population evaluation results indicate a Pearson’s correlation factor of 0.58 in the ABP case, and 0.53 in PPG. These findings highlight the need for advanced compensation techniques, in particular for PPG assessment, to achieve clinical-grade accuracy. Full article
(This article belongs to the Special Issue Advances in Signal and Image Processing for Biomedical Applications)
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24 pages, 6705 KiB  
Article
Three-Dimensional Modeling of Heart Soft Tissue Motion
by Mingzhe Liu, Xuan Zhang, Bo Yang, Zhengtong Yin, Shan Liu, Lirong Yin and Wenfeng Zheng
Appl. Sci. 2023, 13(4), 2493; https://doi.org/10.3390/app13042493 - 15 Feb 2023
Cited by 41 | Viewed by 1566
Abstract
The modeling and simulation of biological tissue is the core part of a virtual surgery system. In this study, the geometric and physical methods related to soft tissue modeling were investigated. Regarding geometric modeling, the problem of repeated inverse calculations of control points [...] Read more.
The modeling and simulation of biological tissue is the core part of a virtual surgery system. In this study, the geometric and physical methods related to soft tissue modeling were investigated. Regarding geometric modeling, the problem of repeated inverse calculations of control points in the Bezier method was solved via re-parameterization, which improved the calculation speed. The base surface superposition method based on prior information was proposed to make the deformation model not only have the advantages of the Bezier method but also have the ability to fit local irregular deformation surfaces. Regarding physical modeling, the fitting ability of the particle spring model to the anisotropy of soft tissue was improved by optimizing the topological structure of the particle spring model. Then, the particle spring model had a more extensive nonlinear fitting ability through the dynamic elastic coefficient parameter. Finally, the secondary modeling of the elastic coefficient based on the virtual body spring enabled the model to fit the creep and relaxation characteristics of biological tissue according to the elongation of the virtual body spring. Full article
(This article belongs to the Special Issue Advances in Signal and Image Processing for Biomedical Applications)
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21 pages, 3842 KiB  
Article
An Improved Stereo Matching Algorithm Based on Joint Similarity Measure and Adaptive Weights
by Xiangjun Lai, Bo Yang, Botao Ma, Mingzhe Liu, Zhengtong Yin, Lirong Yin and Wenfeng Zheng
Appl. Sci. 2023, 13(1), 514; https://doi.org/10.3390/app13010514 - 30 Dec 2022
Cited by 27 | Viewed by 1914
Abstract
Stereo matching is the operation of obtaining the parallax value between two images by matching all the corresponding image points in the two images, thus obtaining the dense parallax image between the two images. How to obtain accurate disparity images has always been [...] Read more.
Stereo matching is the operation of obtaining the parallax value between two images by matching all the corresponding image points in the two images, thus obtaining the dense parallax image between the two images. How to obtain accurate disparity images has always been a key point in the field of stereo vision. Presently, in the research of 3D reconstruction technology based on binocular stereo vision, the main research direction of domestic and foreign scholars is to improve the efficiency and accuracy of stereo matching, and there is research literature on soft tissues. This paper proposes an improved stereo matching algorithm based on joint similarity measures and adaptive weights. The algorithm improves the matching cost calculation based on the joint similarity measure to fit the color image of the heart soft tissue. At the same time, the algorithm uses the idea of graph cutting to improve the adaptive weight. The experimental results show that both the improved joint similarity measure and the improved adaptive weight can effectively reduce the mismatch rate. In addition, the corresponding matching effect is better than using only one of the improved joint similarity measures. Full article
(This article belongs to the Special Issue Advances in Signal and Image Processing for Biomedical Applications)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: THE FAILURE OF BIOMARKERS TO PREDICT THE HIGH ALLERGIC RISK NEONATE AND INFANT : THE CAPE TOWN DATA
Authors: MATTHIAS HAUS
Affiliation: Department of Paediatrics, University of Pretoria, South Africa

Title: 3D color multimodality fusion imaging as an educational and surgical planning tool for extracerebral tumors
Authors: Xiaolin Hou; Ru xiang Xu; Dongdong Yang; Dingjun Li
Affiliation: Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital; Hospital of Chengdu University of Traditional Chinese Medicine
Abstract: BACKGROUND Extracerebral tumors often occur on the surface of the brain or at the skull base. It is particularly important to identify the peritumoral sulci, gyri, and nerve fibers. Preoperative visualization of three-dimensional (3D) multimodal fusion imaging (MFI) is crucial for extracerebral tumor surgery. However, the traditional 3D-MFI brain models are homochromatic and do not allow easy identification of anatomical functional areas.METHODS In this study, 33 patients with extracerebral tumors without peritumoral edema were retrospectively recruited. They underwent 3D T1-weighted MRI, Diffusion tensor imaging (DTI), and CT angiography (CTA) sequence scans. 3DSlicer, Freesurfer, and BrainSuite were used to explore 3D-color-MFI and preoperative planning for those patients. To determine the effectiveness of 3D-color-MFI as a teaching tool for neurosurgeons as well as a patient education and communication tool, questionnaires were administered to 15 neurosurgery residents and all patients, respectively. RESULTS For neurosurgical residents, 3D-color-MFI enabled a better comprehension of surgical anatomy and more efficient techniques for removing extracerebral tumors than traditional 3D-MFI (P<0.001). For patients, utilizing 3D-color-MFI can significantly improve their understanding of the surgical approach and risks (P<0.005). CONCLUSIONS 3D-color-MFI is more helpful for learning surgical anatomy, developing surgical strategies, and improving communication with patients than traditional 3D-MFI. 3D-color-MFI is a tool with great promise for extracerebral tumors.

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