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Nektarios A Valous1,2,3, Eckhard Hitzer4, Dragoş Duşe5,6

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Summary
This summary is machine-generated.

Quaternions, a hypercomplex number type, offer versatile image processing for natural and biomedical applications. These methods enhance color, contrast, and machine learning performance in digital pathology without complex data requirements.

Keywords:
2D orthogonal planes splitcolor imagescomputational biomedicinecomputer visionquaternions

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Area of Science:

  • Computer Vision
  • Image Processing
  • Hypercomplex Numbers

Background:

  • Three-dimensional data, such as color images, present unique processing challenges.
  • Hypercomplex numbers, specifically quaternions, provide a mathematical framework for handling such data.

Purpose of the Study:

  • To demonstrate novel image processing workflows using quaternions and the 2D orthogonal planes split framework.
  • To apply these workflows to various natural and biomedical image processing tasks.

Main Methods:

  • Leveraging quaternions and the 2D orthogonal planes split framework for image manipulation.
  • Implementing workflows for image recolorization, decolorization, contrast enhancement, and stain separation/restaining.
  • Integrating these methods into machine learning and deep learning pipelines for histological images.

Main Results:

  • Successful application of quaternion-based workflows to natural and biomedical images.
  • Demonstrated performance gains in machine learning and deep learning for histological image analysis.
  • Achieved comparable or superior results to existing literature methods using non-data-driven approaches.

Conclusions:

  • Quaternion-based image processing offers a computationally accessible and versatile methodology.
  • These methods effectively regulate color appearance and image contrast.
  • The framework shows significant potential for automated processing, digital pathology, and computer vision applications.