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Diagnostics
Special Issues
Advanced Image and Video Analytics for Biomedical Applications
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Advanced Image and Video Analytics for Biomedical Applications

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 8023

Special Issue Editor

Dr. Mohd Asyraf Zulkifley

E-Mail Website
Guest Editor
Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
Interests: biomedical analytics; deep learning; artificial intelligence; machine learning

Special Issue Information

Dear Colleagues, 

This peer-reviewed Special Issue is open for manuscripts that leverage advanced artificial intelligence techniques that include conventional machine learning and deep learning methods in analyzing images and videos for biomedical applications. There is no restriction to the input modality that includes X-ray, CT scan, MRI, anterior photographed image, fundus image, and many more. We welcome an article that aims to propose and describe state-of-the-art techniques for screening or diagnosing diseases by using an intelligent system. Furthermore, the manuscript can cover various submodules of automated screening and diagnosing systems that include semantic segmentation, data augmentation, advanced pre-processing methods, image normalization techniques, disease classification, symptom regression, disease severity level prediction, and others. Moreover, we also welcome any review on the state-of-the-art screening and diagnosing technology of any disease, especially on advanced deep learning-based topics. This issue will be of interest to biomedical engineers, data-driven physicians, computer science researchers, data scientists, and machine learning engineers. All manuscripts submitted for consideration will be subject to the peer review process in a manner identical to the manuscripts submitted to Diagnostics.

Dr. Mohd Asyraf Zulkifley
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. Diagnostics 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

  • biomedical image processing
  • intelligent biomedical systems
  • smart disease screening systems
  • smart disease diagnosis systems
  • artificial intelligence in healthcare
  • machine learning in healthcare
  • deep learning in healthcare

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

Published Papers (2 papers)

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Research

27 pages, 24049 KiB  
Article
Enhancement of Ultrasound B-Mode Image Quality Using Nonlinear Filtered-Multiply-and-Sum Compounding for Improved Carotid Artery Segmentation
by Asraf Mohamed Moubark, Luzhen Nie, Mohd Hairi Mohd Zaman, Mohammad Tariqul Islam, Mohd Asyraf Zulkifley, Mohd Hafiz Baharuddin, Zainab Alomari and Steven Freear
Diagnostics 2023, 13(6), 1161; https://doi.org/10.3390/diagnostics13061161 - 18 Mar 2023
Viewed by 2625
Abstract
In ultrasound B-mode imaging, the axial resolution (AR) is commonly determined by the duration or bandwidth of an excitation signal. A shorter-duration pulse will produce better resolution compared to a longer one but with compromised penetration depth. Instead of relying on the pulse [...] Read more.
In ultrasound B-mode imaging, the axial resolution (AR) is commonly determined by the duration or bandwidth of an excitation signal. A shorter-duration pulse will produce better resolution compared to a longer one but with compromised penetration depth. Instead of relying on the pulse duration or bandwidth to improve the AR, an alternative method termed filtered multiply and sum (FMAS) has been introduced in our previous work. For spatial-compounding, FMAS uses the autocorrelation technique as used in filtered-delay multiply and sum (FDMAS), instead of conventional averaging. FMAS enables a higher frame rate and less computational complexity than conventional plane-wave compound imaging beamformed with delay and sum (DAS) and FDMAS. Moreover, it can provide an improved contrast ratio and AR. In previous work, no explanation was given on how FMAS was able to improve the AR. Thus, in this work, we discuss in detail the theory behind the proposed FMAS algorithm and how it is able to improve the spatial resolution mainly in the axial direction. Simulations, experimental phantom measurements and in vivo studies were conducted to benchmark the performance of the proposed method. We also demonstrate how the suggested new algorithm may be used in a practical biomedical imaging application. The balloon snake active contour segmentation technique was applied to the ultrasound B-mode image of a common carotid artery produced with FMAS. The suggested method is capable of reducing the number of iterations for the snake to settle on the region-of-interest contour, accelerating the segmentation process. Full article
(This article belongs to the Special Issue Advanced Image and Video Analytics for Biomedical Applications)
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18 pages, 5323 KiB  
Article
Performance Analysis of a Novel Hybrid Segmentation Method for Polycystic Ovarian Syndrome Monitoring
by Asma’ Amirah Nazarudin, Noraishikin Zulkarnain, Siti Salasiah Mokri, Wan Mimi Diyana Wan Zaki, Aini Hussain, Mohd Faizal Ahmad and Ili Najaa Aimi Mohd Nordin
Diagnostics 2023, 13(4), 750; https://doi.org/10.3390/diagnostics13040750 - 16 Feb 2023
Cited by 13 | Viewed by 4713
Abstract
Experts have used ultrasound imaging to manually determine follicle count and perform measurements, especially in cases of polycystic ovary syndrome (PCOS). However, due to the laborious and error-prone process of manual diagnosis, researchers have explored and developed medical image processing techniques to help [...] Read more.
Experts have used ultrasound imaging to manually determine follicle count and perform measurements, especially in cases of polycystic ovary syndrome (PCOS). However, due to the laborious and error-prone process of manual diagnosis, researchers have explored and developed medical image processing techniques to help with diagnosing and monitoring PCOS. This study proposes a combination of Otsu’s thresholding with the Chan–Vese method to segment and identify follicles in the ovary with reference to ultrasound images marked by a medical practitioner. Otsu’s thresholding highlights the pixel intensities of the image and creates a binary mask for use with the Chan–Vese method to define the boundary of the follicles. The acquired results were compared between the classical Chan–Vese method and the proposed method. The performances of the methods were evaluated in terms of accuracy, Dice score, Jaccard index and sensitivity. In overall segmentation evaluation, the proposed method showed superior results compared to the classical Chan–Vese method. Among the calculated evaluation metrics, the sensitivity of the proposed method was superior, with an average of 0.74 ± 0.12. Meanwhile, the average sensitivity for the classical Chan–Vese method was 0.54 ± 0.14, which is 20.03% lower than the sensitivity of the proposed method. Moreover, the proposed method showed significantly improved Dice score (p = 0.011), Jaccard index (p = 0.008) and sensitivity (p = 0.0001). This study showed that the combination of Otsu’s thresholding and the Chan–Vese method enhanced the segmentation of ultrasound images. Full article
(This article belongs to the Special Issue Advanced Image and Video Analytics for Biomedical Applications)
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