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Multimodal Image Analysis with Advanced Computational Intelligence
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Multimodal Image Analysis with Advanced Computational Intelligence

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

Deadline for manuscript submissions: closed (30 December 2023) | Viewed by 4471

Special Issue Editors

Prof. Dr. Jing Cai

E-Mail Website
Guest Editor
Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong 999077, China
Interests: medical imaging; AI; radiotherapy
Dr. Yuanpeng Zhang

E-Mail Website
Guest Editor
Department of Medical Informatics, Nantong University, Nantong 226007, China
Interests: machine learning; fuzzy systems; multi-view learning; transfer learning

Special Issue Information

Dear Colleagues,

Biomedical sensors and imaging devices can be used to obtain data on different modalities in clinical settings. For example, lesion features can be obtained by MR imaging and CT imaging. The application of these techniques may reveal differences between various modalities, which can provide incidental supplementary references for diagnosis and/or prognosis.

In recent years, advanced intelligence techniques such as multi-view learning and multi-task learning have provided solutions enabling the reasonable and effective exploitation of the differences between these modalities. Such advanced computational intelligence techniques can be employed to obtain consistent and complementary representations of various modalities by mining the differences between them, thus improving the performance of decision making.

This Special Issue aims to present high-quality research and review manuscripts focusing on multimodal biomedical image fusion. The topics of interest include, but are not limited to, the following:

  • Multimodal medical image registration using advanced computational intelligence
  • Multi-task/-view learning for medical-image-based decision making
  • Biomedical image feature extraction and reduction
  • Advanced biomedical image fusion algorithms

Prof. Dr. Jing Cai
Dr. Yuanpeng Zhang
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

  • multimodal fusion
  • multi-view/-task learning
  • multimodal registration
  • advanced computational intelligence

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Published Papers (2 papers)

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Research

14 pages, 3622 KiB  
Article
AI-Based Approach to One-Click Chronic Subdural Hematoma Segmentation Using Computed Tomography Images
by Andrey Petrov, Alexey Kashevnik, Mikhail Haleev, Ammar Ali, Arkady Ivanov, Konstantin Samochernykh, Larisa Rozhchenko and Vasiliy Bobinov
Sensors 2024, 24(3), 721; https://doi.org/10.3390/s24030721 - 23 Jan 2024
Cited by 4 | Viewed by 2045
Abstract
This paper presents a computer vision-based approach to chronic subdural hematoma segmentation that can be performed by one click. Chronic subdural hematoma is estimated to occur in 0.002–0.02% of the general population each year and the risk increases with age, with a high [...] Read more.
This paper presents a computer vision-based approach to chronic subdural hematoma segmentation that can be performed by one click. Chronic subdural hematoma is estimated to occur in 0.002–0.02% of the general population each year and the risk increases with age, with a high frequency of about 0.05–0.06% in people aged 70 years and above. In our research, we developed our own dataset, which includes 53 series of CT scans collected from 21 patients with one or two hematomas. Based on the dataset, we trained two neural network models based on U-Net architecture to automate the manual segmentation process. One of the models performed segmentation based only on the current frame, while the other additionally processed multiple adjacent images to provide context, a technique that is more similar to the behavior of a doctor. We used a 10-fold cross-validation technique to better estimate the developed models’ efficiency. We used the Dice metric for segmentation accuracy estimation, which was 0.77. Also, for testing our approach, we used scans from five additional patients who did not form part of the dataset, and created a scenario in which three medical experts carried out a hematoma segmentation before we carried out segmentation using our best model. We developed the OsiriX DICOM Viewer plugin to implement our solution into the segmentation process. We compared the segmentation time, which was more than seven times faster using the one-click approach, and the experts agreed that the segmentation quality was acceptable for clinical usage. Full article
(This article belongs to the Special Issue Multimodal Image Analysis with Advanced Computational Intelligence)
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15 pages, 2979 KiB  
Article
Epileptic EEG Signal Detection Using Variational Modal Decomposition and Improved Grey Wolf Algorithm
by Yongxin Sun and Xiaojuan Chen
Sensors 2023, 23(19), 8078; https://doi.org/10.3390/s23198078 - 25 Sep 2023
Cited by 4 | Viewed by 1657
Abstract
Epilepsy does great harm to the human body, and even threatens human life when it is serious. Therefore, research focused on the diagnosis and treatment of epilepsy holds paramount clinical significance. In this paper, we utilized variational modal decomposition (VMD) and an enhanced [...] Read more.
Epilepsy does great harm to the human body, and even threatens human life when it is serious. Therefore, research focused on the diagnosis and treatment of epilepsy holds paramount clinical significance. In this paper, we utilized variational modal decomposition (VMD) and an enhanced grey wolf algorithm to detect epileptic electroencephalogram (EEG) signals. Data were extracted from each patient’s preseizure period and seizure period of 200 s each, with every 2 s as a segment, meaning 100 data points could be obtained for each patient’s health period as well as 100 data points for each patient’s epilepsy period. Variational modal decomposition (VMD) was used to obtain the corresponding intrinsic modal function (VMF) of the data. Then, the differential entropy (DE) and high frequency detection (HFD) of each VMF were extracted as features. The improved grey wolf algorithm is adopted for a selected channel to improve the maximum value of the channel. Finally, the EEG signal samples were classified using a support vector machine (SVM) classifier to achieve the accurate detection of epilepsy EEG signals. Experimental results show that the accuracy, sensitivity and specificity of the proposed method can reach 98.3%, 98.9% and 98.5%, respectively. The proposed algorithm in this paper can be used as an index to detect epileptic seizures and has certain guiding significance for the early diagnosis and effective treatment of epileptic patients. Full article
(This article belongs to the Special Issue Multimodal Image Analysis with Advanced Computational Intelligence)
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