Statistical Biomedical Signal and Image Processing and Understanding: 2nd Edition

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "Medical Imaging".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 3693

Special Issue Editor


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Guest Editor
CNRS, Centrale Marseille, Institut Fresnel UMR 7249, Aix-Marseille University, 13007 Marseille, France
Interests: biomedical image processing; computer-aided diagnosis; pattern recognition; machine learning for medical applications
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Special Issue Information

Dear Colleagues,

Statistical methods, applied to image analysis, are an important research topic in the field of image processing. Medical and biological images are natural images where statistical processing approaches could help doctors and biologists to better understand, detect and diagnose using computer-based methods. Recently, deep learning techniques have given an impressive performance in recognition and classification tasks. Therefore, a great number of research studies turned to use neural networks to address the recent trend of machine learning. This Special Issue focuses on new statistical methods applied to signal and images in the biomedical field, including all biomedical modalities, and it welcomes submissions of theoretical or application-based approaches related, but not limited, to:

  • Multi-dimensional imaging;
  • Medical images filtering;
  • Restoration;
  • Segmentation and registration;
  • Feature extraction and image description;
  • Classification tools for image-based diagnosis;
  • Methods in diagnosis optimization;
  • Computer-aided diagnosis systems and methods;
  • Deep learning.

Prof. Dr. Mouloud Adel
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. Journal of Imaging is an international peer-reviewed open access monthly 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 1800 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 signal processing
  • medical image processing
  • microscopy
  • image segmentation, registration and visualization
  • classification
  • hyperspectral biomedical images
  • image interpretation
  • texture analysis
  • machine learning
  • biometrics

Published Papers (2 papers)

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14 pages, 2879 KiB  
Article
Extended Analysis of Raman Spectra Using Artificial Intelligence Techniques for Colorectal Abnormality Classification
by Dimitris Kalatzis, Ellas Spyratou, Maria Karnachoriti, Maria Anthi Kouri, Ioannis Stathopoulos, Nikolaos Danias, Nikolaos Arkadopoulos, Spyros Orfanoudakis, Ioannis Seimenis, Athanassios G. Kontos and Efstathios P. Efstathopoulos
J. Imaging 2023, 9(12), 261; https://doi.org/10.3390/jimaging9120261 - 24 Nov 2023
Cited by 1 | Viewed by 1618
Abstract
Raman spectroscopy (RS) techniques are attracting attention in the medical field as a promising tool for real-time biochemical analyses. The integration of artificial intelligence (AI) algorithms with RS has greatly enhanced its ability to accurately classify spectral data in vivo. This combination has [...] Read more.
Raman spectroscopy (RS) techniques are attracting attention in the medical field as a promising tool for real-time biochemical analyses. The integration of artificial intelligence (AI) algorithms with RS has greatly enhanced its ability to accurately classify spectral data in vivo. This combination has opened up new possibilities for precise and efficient analysis in medical applications. In this study, healthy and cancerous specimens from 22 patients who underwent open colorectal surgery were collected. By using these spectral data, we investigate an optimal preprocessing pipeline for statistical analysis using AI techniques. This exploration entails proposing preprocessing methods and algorithms to enhance classification outcomes. The research encompasses a thorough ablation study comparing machine learning and deep learning algorithms toward the advancement of the clinical applicability of RS. The results indicate substantial accuracy improvements using techniques like baseline correction, L2 normalization, filtering, and PCA, yielding an overall accuracy enhancement of 15.8%. In comparing various algorithms, machine learning models, such as XGBoost and Random Forest, demonstrate effectiveness in classifying both normal and abnormal tissues. Similarly, deep learning models, such as 1D-Resnet and particularly the 1D-CNN model, exhibit superior performance in classifying abnormal cases. This research contributes valuable insights into the integration of AI in medical diagnostics and expands the potential of RS methods for achieving accurate malignancy classification. Full article
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9 pages, 1322 KiB  
Brief Report
Testicular Evaluation Using Shear Wave Elastography (SWE) in Patients with Varicocele
by Sandra Baleato-Gonzalez, Iria Osorio-Vazquez, Enrique Flores-Ríos, María Isolina Santiago-Pérez, Juan Pablo Laguna-Reyes and Roberto Garcia-Figueiras
J. Imaging 2023, 9(9), 166; https://doi.org/10.3390/jimaging9090166 - 22 Aug 2023
Cited by 1 | Viewed by 1541
Abstract
Purpose: To assess the possible influence of the presence of varicocele on the quantification of testicular stiffness. Methods: Ultrasound with shear wave elastography (SWE) was performed on 48 consecutive patients (96 testicles) referred following urology consultation for different reasons. A total of 94 [...] Read more.
Purpose: To assess the possible influence of the presence of varicocele on the quantification of testicular stiffness. Methods: Ultrasound with shear wave elastography (SWE) was performed on 48 consecutive patients (96 testicles) referred following urology consultation for different reasons. A total of 94 testes were studied and distributed in three groups: testes with varicocele (group A, n = 19), contralateral normal testes (group B; n = 13) and control group (group C, n = 62). Age, testicular volume and testicular parenchymal tissue stiffness values of the three groups were compared using the Kruskal–Wallis test. Results: The mean age of the patients was 42.1 ± 11.1 years. The main reason for consultation was infertility (64.6%). The mean SWE value was 4 ± 0.4 kPa (kilopascal) in group A, 4 ± 0.5 kPa in group B and 4.2 ± 0.7 kPa in group C or control. The testicular volume was 15.8 ± 3.8 mL in group A, 16 ± 4.3 mL in group B and 16.4 ± 5.9 mL in group C. No statistically significant differences were found between the three groups in terms of age, testicular volume and tissue stiffness values. Conclusion: Tissue stiffness values were higher in our control group (healthy testicles) than in patients with varicocele. Full article
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