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Applied Sciences
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Novel Insights into Medical Images Processing
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Novel Insights into Medical Images Processing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: 20 December 2025 | Viewed by 669

Special Issue Editors

Dr. Irazú Hernández Farías

E-Mail Website
Guest Editor
Department of Computational Science, Instituto Nacional de Astrofisica, Optica y Electronica (INAOE), Puebla 72840, Mexico
Interests: natural language processing; machine learning; affective information; irony detection; sentiment analysis
Dr. Hayde Peregrina-Barreto

E-Mail Website
Guest Editor
Departamento de Ciencias Computacionales, Instituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro 1, Santa Maria Tonantzintla, San Andres Cholula 72840, Mexico
Interests: image processing; medical imaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advances in medical technology have generated new sources of information used to understand the behavior of several diseases through images. The analysis of medical images is important for processing information from different tasks, such as the use of CAD tools, diagnosis assistance, monitoring, individual treatments, remote health care, and case identification, among others. Interdisciplinary research is of utmost importance to continue to improve our knowledge and the development of new technologies. Moreover, access to new images has allowed us to observe information beyond our visual abilities.

However, this new information facilitates the analysis and identification of health conditions. We must use new optical, computational, and electronic methods to acquire and study these medical images.

This Special Issue aims to compile new contributions to medical image processing from acquisition to analysis and interpretation. Contributions from other areas and multidisciplinary research are welcome. We warmly encourage you to contribute to this collection in Applied Sciences.

Areas of focus include, but are not limited to, image processing and analysis; medical image devices; image classification and segmentation; machine learning and deep learning for medical images; optical and photonics biosensing; development of CADe and CADx tools; medical measurements and instrumentation; biomedical image analysis; virtual reality in medicine; IoT in medicine; biosignal processing; and embedded systems.

Dr. Irazú Hernández Farías
Dr. Hayde Peregrina-Barreto
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 2400 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

  • image processing
  • image analysis
  • medical image devices
  • image classification
  • image segmentation

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Published Papers (2 papers)

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17 pages, 23834 KiB  
Article
Information Merging for Improving Automatic Classification of Electrical Impedance Mammography Images
by Jazmin Alvarado-Godinez, Hayde Peregrina-Barreto, Delia Irazú Hernández-Farías and Blanca Murillo-Ortiz
Appl. Sci. 2025, 15(14), 7735; https://doi.org/10.3390/app15147735 - 10 Jul 2025
Viewed by 124
Abstract
Breast cancer remains one of the leading causes of mortality among women worldwide, highlighting the critical need for early and accurate detection methods. Traditional mammography, although widely used, has limitations, including radiation exposure and challenges in detecting early-stage lesions. Electrical Impedance Mammography (EIM) [...] Read more.
Breast cancer remains one of the leading causes of mortality among women worldwide, highlighting the critical need for early and accurate detection methods. Traditional mammography, although widely used, has limitations, including radiation exposure and challenges in detecting early-stage lesions. Electrical Impedance Mammography (EIM) has emerged as a non-invasive and radiation-free alternative that assesses the density and electrical conductivity of breast tissue. EIM images consist of seven layers, each representing different tissue depths, offering a detailed representation of the breast structure. However, analyzing these layers individually can be redundant and complex, making it difficult to identify relevant features for lesion classification. To address this issue, advanced computational techniques are employed for image integration, such as the Root Mean Square (CRMS) Contrast and Contrast-Limited Adaptive Histogram Equalization (CLAHE), combined with the Coefficient of Variation (CV), CLAHE-based fusion, weighted average fusion, Gaussian pyramid fusion, and Wavelet–PCA fusion. Each method enhances the representation of tissue features, optimizing the image quality and diagnostic utility. This study evaluated the impact of these integration techniques on EIM image analysis, aiming to improve the accuracy and reliability of computational diagnostic models for breast cancer detection. According to the obtained results, the best performance was achieved using Wavelet–PCA fusion in combination with XGBoost as a classifier, yielding an accuracy rate of 89.5% and an F1-score of 81.5%. These results are highly encouraging for the further investigation of this topic. Full article
(This article belongs to the Special Issue Novel Insights into Medical Images Processing)
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17 pages, 2781 KiB  
Article
Enhancing AI-Driven Diagnosis of Invasive Ductal Carcinoma with Morphologically Guided and Interpretable Deep Learning
by Suphakon Jarujunawong and Paramate Horkaew
Appl. Sci. 2025, 15(12), 6883; https://doi.org/10.3390/app15126883 - 18 Jun 2025
Viewed by 284
Abstract
Artificial intelligence is increasingly shaping the landscape of computer-aided diagnosis of breast cancer. Despite incrementally improved accuracy, pathologist supervision remains essential for verified interpretation. While prior research focused on devising deep model architecture, this study examines the pivotal role of multi-band visual-enhanced features [...] Read more.
Artificial intelligence is increasingly shaping the landscape of computer-aided diagnosis of breast cancer. Despite incrementally improved accuracy, pathologist supervision remains essential for verified interpretation. While prior research focused on devising deep model architecture, this study examines the pivotal role of multi-band visual-enhanced features in invasive ductal carcinoma classification using whole slide imaging. Our results showed that orientation invariant filters achieved an accuracy of 0.8125, F1-score of 0.8134, and AUC of 0.8761, while preserving cellular arrangement and tissue morphology. By utilizing spatial relationships across varying extents, the proposed fusion strategy aligns with pathological interpretation principles. While integrating Gabor wavelet responses into ResNet-50 enhanced feature association, the comparative analysis emphasized the benefits of weighted morphological fusion, further strengthening diagnostic performance. These insights underscore the crucial role of informative filters in advancing DL schemes for breast cancer screening. Future research incorporating diverse, multi-center datasets could further validate the approach and broaden its diagnostic applications. Full article
(This article belongs to the Special Issue Novel Insights into Medical Images Processing)
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