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Biomedical Image Processing Based on Artificial Intelligence and Pattern Recognition Using Sensors

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

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 3866

Special Issue Editors

Dr. Yuk-Ming Tang

E-Mail Website
Guest Editor
Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong
Interests: virtual reality; artificial intelligence; digital twin; Industry 4.0; sustainable technology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Andrew W. H. Ip

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A2, Canada
Interests: radio frequency identification (RFID) and supply chain technologies; wireless sensor networks (WSNs); decision support systems and artificial intelligence (AI); big data analytics; resilience engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biomedical image processing is one of the emergent research in the healthcare, medical and engineering fields developed over the past decades. With the recent advanced in artificial intelligence technologies such as deep learning and machine learning, as well as the pattern recognition algorithm, biomedical image processing has been rapidly witnessed a tremendous development of new and advanced tools and instruments for collecting, recognizing, storing, transmitting, analyzing, and displaying medical images. This change has led to significant growth of application in the multiple fields for solving specific biomedical problems including but not limited to recognition, segmentation, parsing, visualization, feature extraction, image-guided surgery, shape measurements, computer modelling and simulation, modeling for virtual and augmented reality, telemedicine, etc. This special issue provides a forum for experts to disseminate the latest research findings and results in the field. The journal specialized in research related to the applications of pattern recognition, AI, computer vision, and virtual reality to biomedical image progressing problems. We accept papers that cover the development and implementation of algorithms and strategies to tackle and solve state-of-the-art problems in the field.

Dr. Yuk-Ming Tang
Prof. Dr. Andrew W.H. Ip
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 imaging
  • image processing
  • segmentation
  • artificial intelligence
  • pattern recognition
  • machine learning
  • healthcare
  • virtual reality

Published Papers (2 papers)

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Research

21 pages, 9324 KiB  
Article
Sensor-to-Image Based Neural Networks: A Reliable Reconstruction Method for Diffuse Optical Imaging of High-Scattering Media
by Diannata Rahman Yuliansyah, Min-Chun Pan and Ya-Fen Hsu
Sensors 2022, 22(23), 9096; https://doi.org/10.3390/s22239096 - 23 Nov 2022
Cited by 4 | Viewed by 1206
Abstract
Imaging tasks today are being increasingly shifted toward deep learning-based solutions. Biomedical imaging problems are no exception toward this tendency. It is appealing to consider deep learning as an alternative to such a complex imaging task. Although research of deep learning-based solutions continues [...] Read more.
Imaging tasks today are being increasingly shifted toward deep learning-based solutions. Biomedical imaging problems are no exception toward this tendency. It is appealing to consider deep learning as an alternative to such a complex imaging task. Although research of deep learning-based solutions continues to thrive, challenges still remain that limits the availability of these solutions in clinical practice. Diffuse optical tomography is a particularly challenging field since the problem is both ill-posed and ill-conditioned. To get a reconstructed image, various regularization-based models and procedures have been developed in the last three decades. In this study, a sensor-to-image based neural network for diffuse optical imaging has been developed as an alternative to the existing Tikhonov regularization (TR) method. It also provides a different structure compared to previous neural network approaches. We focus on realizing a complete image reconstruction function approximation (from sensor to image) by combining multiple deep learning architectures known in imaging fields that gives more capability to learn than the fully connected neural networks (FCNN) and/or convolutional neural networks (CNN) architectures. We use the idea of transformation from sensor- to image-domain similarly with AUTOMAP, and use the concept of an encoder, which is to learn a compressed representation of the inputs. Further, a U-net with skip connections to extract features and obtain the contrast image, is proposed and implemented. We designed a branching-like structure of the network that fully supports the ring-scanning measurement system, which means it can deal with various types of experimental data. The output images are obtained by multiplying the contrast images with the background coefficients. Our network is capable of producing attainable performance in both simulation and experiment cases, and is proven to be reliable to reconstruct non-synthesized data. Its apparent superior performance was compared with the results of the TR method and FCNN models. The proposed and implemented model is feasible to localize the inclusions with various conditions. The strategy created in this paper can be a promising alternative solution for clinical breast tumor imaging applications. Full article
(This article belongs to the Special Issue Biomedical Image Processing Based on Artificial Intelligence and Pattern Recognition Using Sensors)
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12 pages, 3686 KiB  
Article
A Fissure-Aided Registration Approach for Automatic Pulmonary Lobe Segmentation Using Deep Learning
by Mengfan Xue, Lu Han, Yiran Song, Fan Rao and Dongliang Peng
Sensors 2022, 22(21), 8560; https://doi.org/10.3390/s22218560 - 07 Nov 2022
Cited by 3 | Viewed by 1683
Abstract
The segmentation of pulmonary lobes is important in clinical assessment, lesion location, and surgical planning. Automatic lobe segmentation is challenging, mainly due to the incomplete fissures or the morphological variation resulting from lung disease. In this work, we propose a learning-based approach that [...] Read more.
The segmentation of pulmonary lobes is important in clinical assessment, lesion location, and surgical planning. Automatic lobe segmentation is challenging, mainly due to the incomplete fissures or the morphological variation resulting from lung disease. In this work, we propose a learning-based approach that incorporates information from the local fissures, the whole lung, and priori pulmonary anatomy knowledge to separate the lobes robustly and accurately. The prior pulmonary atlas is registered to the test CT images with the aid of the detected fissures. The result of the lobe segmentation is obtained by mapping the deformation function on the lobes-annotated atlas. The proposed method is evaluated in a custom dataset with COPD. Twenty-four CT scans randomly selected from the custom dataset were segmented manually and are available to the public. The experiments showed that the average dice coefficients were 0.95, 0.90, 0.97, 0.97, and 0.97, respectively, for the right upper, right middle, right lower, left upper, and left lower lobes. Moreover, the comparison of the performance with a former learning-based segmentation approach suggests that the presented method could achieve comparable segmentation accuracy and behave more robustly in cases with morphological specificity. Full article
(This article belongs to the Special Issue Biomedical Image Processing Based on Artificial Intelligence and Pattern Recognition Using Sensors)
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