Artificial Intelligence for Biomedical Image Processing and Data Analysis

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 31 October 2024 | Viewed by 540

Special Issue Editor


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Guest Editor
Department of Computer Science, Brunel University London, Uxbridge UB8 3PH, UK
Interests: artificial intelligence; data science; image processing; medical imaging; automatic control

Special Issue Information

Dear Colleagues,

We are pleased to announce this Special Issue of the journal Mathematics, entitled “Artificial Intelligence for Biomedical Image Processing and Data Analysis”. This Special Issue focuses on the application of mathematical principles and techniques in the intersection of artificial intelligence (AI), medical imaging, and data analysis. Artificial intelligence has revolutionized the field of biomedical image analysis by enabling automated and accurate interpretation of medical images. It encompasses various AI techniques such as machine learning, deep learning, and computer vision, which can extract meaningful information from complex medical images and aid in diagnosis, treatment planning, and disease monitoring. Moreover, data analysis plays a crucial role in the medical domain as it involves processing and interpreting large volumes of biomedical data to discover patterns, identify trends, and make informed decisions. We welcome contributions from researchers and practitioners in the field. The submission of papers addressing various aspects of AI for biomedical image processing and data analysis is encouraged. Topics of interest include image segmentation, image processing, feature extraction, object detection and computer vision, and machine-learning-based approaches for medical image analysis and data science.

Dr. Yongmin Li
Guest Editor

Manuscript Submission Information

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Keywords

  • artificial intelligence
  • machine learning
  • data science
  • image processing
  • image segmentation
  • medical imaging
  • computer vision
  • biomedical engineering
  • healthcare technologies

Published Papers (1 paper)

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Research

19 pages, 2006 KiB  
Article
Shearlet Transform Applied to a Prostate Cancer Radiomics Analysis on MR Images
by Rosario Corso, Alessandro Stefano, Giuseppe Salvaggio and Albert Comelli
Mathematics 2024, 12(9), 1296; https://doi.org/10.3390/math12091296 - 25 Apr 2024
Viewed by 251
Abstract
For decades, wavelet theory has attracted interest in several fields in dealing with signals. Nowadays, it is acknowledged that it is not very suitable to face aspects of multidimensional data like singularities and this has led to the development of other mathematical tools. [...] Read more.
For decades, wavelet theory has attracted interest in several fields in dealing with signals. Nowadays, it is acknowledged that it is not very suitable to face aspects of multidimensional data like singularities and this has led to the development of other mathematical tools. A recent application of wavelet theory is in radiomics, an emerging field aiming to improve diagnostic, prognostic and predictive analysis of various cancer types through the analysis of features extracted from medical images. In this paper, for a radiomics study of prostate cancer with magnetic resonance (MR) images, we apply a similar but more sophisticated tool, namely the shearlet transform which, in contrast to the wavelet transform, allows us to examine variations along more orientations. In particular, we conduct a parallel radiomics analysis based on the two different transformations and highlight a better performance (evaluated in terms of statistical measures) in the use of the shearlet transform (in absolute value). The results achieved suggest taking the shearlet transform into consideration for radiomics studies in other contexts. Full article
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