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ORIGINAL RESEARCH article

Front. Earth Sci.
Sec. Geoinformatics
Volume 13 - 2025 | doi: 10.3389/feart.2025.1508690

From Rocks to Pixels: A Comprehensive Framework for Grain Shape Characterization through the Image Analysis of Size, Orientation, and Form Descriptors

Provisionally accepted
Arnaud L. Back Arnaud L. Back 1*Cyril Kana Tepakbong Cyril Kana Tepakbong 1L. Paul Bédard L. Paul Bédard 1Amadou Barry Amadou Barry 2
  • 1 Université du Québec à Chicoutimi, Chicoutimi, Canada
  • 2 Centre Armand-Frappier Santé Biotechnologie, Institut National de la Recherche Scientifique, Université du Québec, Laval, Quebec, Canada

The final, formatted version of the article will be published soon.

    Accurately describing grain shapes is crucial in geology, mineral exploration, civil engineering, and other sciences. Advances in image analysis now allow for easy object separation and quantitative shape description. However, despite extensive applications in sedimentology, chemistry, and civil engineering, there is no consensus on the use of shape descriptors, and their meanings often remain unclear. This article presents a method for quantitatively describing grain shapes at a micrometer-to-centimeter scale using various image analysis techniques. Our approach selects the most appropriate combination of quantitative descriptors to describe grain shape. This work is based on an extensive literature review across many scientific fields to extract multiple quantitative shape measurements. This paper focuses on size, orientation, and form descriptors. A total of 51 descriptors, including elongation and Fourier amplitudes, were extracted, compiled, and computed using Python. The descriptor computation code is provided as a library with this article. We use principal component analysis to select the most significant descriptors and use multiple descriptors without losing clarity. We validated our approach on generated images. Using this combination of principal component analysis and image-based descriptors, we could discriminate 8 of the 13 ideal forms (ranging from a circle to a dodecagon), showcasing the potential precision when running noiseless data. The process was then applied to a sample of 584 galena grains, and we successfully described quantitatively the shape tendencies within this galena grain population. Our results, accompanied by noisy generated images, highlight the strong influence of roundness, roughness, and shape descriptors on each other, which also explains the challenges in identifying the best descriptors.

    Keywords: Quantitative descriptors, shape discrimination, Computer Vision, statistical analysis, image processing, Petrography

    Received: 09 Oct 2024; Accepted: 10 Jan 2025.

    Copyright: © 2025 Back, Kana Tepakbong, Bédard and Barry. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Arnaud L. Back, Université du Québec à Chicoutimi, Chicoutimi, Canada

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.