Special Issue "Image Processing Techniques for Biomedical Applications II"

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

Deadline for manuscript submissions: closed (20 September 2022) | Viewed by 744

Special Issue Editors

Prof. Dr. Cecilia Di Ruberto
E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, University of Cagliari, via Ospedale 72, 09125 Cagliari, Italy
Interests: computer vision; image retrieval; biomedical image analysis; pattern recognition and machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Andrea Loddo
E-Mail Website
Guest Editor
University of Cagliari, Department of Mathematics and Computer Science, via Ospedale 72, 09124 Cagliari, Italy
Interests: computer vision; image retrieval; biomedical image analysis; pattern recognition and machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Lorenzo Putzu
E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d’Armi, 09123 Cagliari, Italy
Interests: computer vision; medical image analysis; shape analysis and matching; image retrieval and classification
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue of the journal Applied Sciences, entitled Image-Processing Techniques for Biomedical Applications II, aims to present recent advances in the generation and utilization of image-processing techniques and future prospects of this fundamental research area. 

In recent years, there has been a growing interest in the creation of powerful biomedical image processing tools to assist medical experts. This is mainly due to the availability of medical data in digital form, which enables a large number of artificial intelligence applications based on classical machine learning and deep learning.

This Special Issue pays special attention to contributions dealing with practical problems, where image processing applications have to deal with challenging scenarios where high inter/intra-patient variability is present.  All interested authors are invited to submit their most recent results on biomedical image processing and analysis for possible publication in this Special Issue. All articles must be original, unpublished work and will be subject to the normal standards and peer review processes of this journal.

Potential topics include, but are not limited to:

  • Medical image reconstruction;
  • Medical image retrieval;
  • Medical image segmentation;
  • Deep or handcrafted features for biomedical image classification;
  • Visualization in biomedical imaging;
  • Machine learning and artificial intelligence;
  • Image analysis of anatomical structures and lesions;
  • Computer-aided detection/diagnosis;
  • Multimodality fusion for diagnosis, image analysis, and image-guided interventions;
  • Combination of image analysis with clinical data mining and analytics;
  • Applications of big data in imaging;
  • Microscopy and histology image analysis;
  • Ophthalmic image analysis;
  • Applications of computational pathology in the clinic.

Prof. Dr. Cecilia Di Ruberto
Dr. Andrea Loddo
Dr. Lorenzo Putzu
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 2300 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 reconstruction
  • medical image retrieval
  • medical image segmentation
  • deep or handcrafted features for biomedical image classification
  • visualization in biomedical imaging
  • machine learning and artificial intelligence
  • image analysis of anatomical structures and lesions
  • computer-aided detection/diagnosis
  • multimodality fusion for diagnosis, image analysis, and image-guided interventions
  • combination of image analysis with clinical data mining and analytics
  • applications of big data in imaging
  • microscopy and histology image analysis
  • ophthalmic image analysis
  • applications of computational pathology in the clinic

Related Special Issue

Published Papers (1 paper)

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Research

Article
Spatial Heterogeneity of Excess Lung Fluid in Cystic Fibrosis: Generalized, Localized Diffuse, and Localized Presentations
Appl. Sci. 2022, 12(20), 10647; https://doi.org/10.3390/app122010647 - 21 Oct 2022
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Abstract
Magnetic resonance (MR) imaging has demonstrated that CF subjects have a significantly higher lung density (e.g., fluid content) when compared with healthy control subjects, but, at present, there are no techniques to quantify the spatial presentation of these lung abnormalities. The excess fluid [...] Read more.
Magnetic resonance (MR) imaging has demonstrated that CF subjects have a significantly higher lung density (e.g., fluid content) when compared with healthy control subjects, but, at present, there are no techniques to quantify the spatial presentation of these lung abnormalities. The excess fluid in MR lung images for CF subjects with mild (n = 4), moderate (n = 5), and severe (n = 4) disease and age- and sex-matched healthy controls (n = 13) in both the right and left lungs was identified and quantified using a thresholding-based image segmentation technique using healthy controls as a baseline. MR lung images were categorized into one of three spatial presentation groups based on their regional and global percent area of the lung covered by excess fluid (i.e., spatial distribution): (i) generalized for sparse, (ii) localized diffuse for a moderate focality, and (iii) localized for a strong focality. A total of 96% of the controls presented as generalized. CF subjects populated all three presentation groups and an individual’s right and left lungs did not always categorize identically. The developed metrics for categorization provide a quantification method to describe the spatial presentation of CF disease and suggests the heterogeneous nature of the disease. Full article
(This article belongs to the Special Issue Image Processing Techniques for Biomedical Applications II)
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