Computational workflows for natural and biomedical image processing based on hypercomplex algebras

Quaternions, a type of hypercomplex number, can be applied to handling three-dimensional data, i.e., color images. Here, we demonstrate, by leveraging quaternions and the two-dimensional orthogonal planes split framework, image processing workflows for natural and biomedical images, including natural and biomedical image recolorization, natural image decolorization, natural and biomedical image contrast enhancement, and computational restaining and stain separation in histological images. We also demonstrate performance gains in machine learning and deep learning pipelines for histological images. The proposed workflows can regulate color appearance and image contrast, be part of automated processing pipelines, and assist in digital pathology applications. Employing basic arithmetic and matrix operations, this work offers a computationally accessible methodology that showcases versatility and consistency across processing tasks and a range of computer vision and biomedical applications. The proposed non-data-driven methods achieve comparable or better results to those reported in the literature, showcasing the potential of robust theoretical frameworks with practical effectiveness.

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Publication Details

Journal
Patterns
Published
2025-10-15
DOI
https://doi.org/10.1016/j.patter.2025.101388
Primary Topic
Digital Image Processing Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

Computational workflows for natural and biomedical image processing based on hypercomplex algebras

Rodrigo Rojas Moraleda, Nektarios A. Valous, Inka Zörnig, Bénédicte Lenoir et al.
Patterns
Digital Image Processing Techniques
article

Computational workflows for natural and biomedical image processing based on hypercomplex algebras

Rodrigo Rojas Moraleda, Nektarios A. Valous, Inka Zörnig, Bénédicte Lenoir, Dirk Jäger, Anna Berthel, Carlo Fremd, Eckhard Hitzer, Meggy Suarez‐Carmona, Niels Halama, Ismini Papageorgiou, D.-M. Duse, Alexander Rölle, Cord Westhoff, Ferdinand Popp
article en

Abstract

Quaternions, a type of hypercomplex number, can be applied to handling three-dimensional data, i.e., color images. Here, we demonstrate, by leveraging quaternions and the two-dimensional orthogonal planes split framework, image processing workflows for natural and biomedical images, including natural and biomedical image recolorization, natural image decolorization, natural and biomedical image contrast enhancement, and computational restaining and stain separation in histological images. We also demonstrate performance gains in machine learning and deep learning pipelines for histological images. The proposed workflows can regulate color appearance and image contrast, be part of automated processing pipelines, and assist in digital pathology applications. Employing basic arithmetic and matrix operations, this work offers a computationally accessible methodology that showcases versatility and consistency across processing tasks and a range of computer vision and biomedical applications. The proposed non-data-driven methods achieve comparable or better results to those reported in the literature, showcasing the potential of robust theoretical frameworks with practical effectiveness.

PatternsVol. 6(11)
Philipps University of Marburg (DE), German Cancer Research Center (DE), Johannes Gutenberg University Mainz (DE), International Christian University (JP), Heidelberg University (DE), Universitätsklinikum Gießen und Marburg (DE), University Hospital Heidelberg (DE), University Medical Center of the Johannes Gutenberg University Mainz (DE), National Center for Tumor Diseases (DE), Helmholtz Institute Mainz (DE), Jena University Hospital (DE), Südharz Klinikum (DE), Spitalul Clinic Județean de Urgență Cluj-Napoca (RO)
Openalex Percentile: Top 71%
Digital Image Processing Techniques
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