From Rocks to Pixels: A Comprehensive Framework for Grain Shape Characterization Through Image Analysis of Roundness and Roughness Descriptors

Arnaud L. Back^1*Cyril KANA Tepakbong¹L.Paul Bedard¹Amadou Barry²

• ¹Université du Québec à Chicoutimi, Chicoutimi, Canada
• ²Institut national de la recherche scientifique, Qubec City, Canada

Geological processes are recorded in grain shape and geochemistry. However, grains are often described with minimal quantification. These descriptions are generally textual and can vary in their precision and accuracy. Historically, detailed studies of crystal size distribution have provided valuable insights into petrogenesis. A thorough analysis of numerous computable grain descriptors will offer even more significant information. Despite extensive literature on shape descriptors in fields like sedimentology, chemistry, and civil engineering, there is no consensus on their use, and their meanings often remain unclear. This article proposes a quantitative grain description method ranging from micrometers to centimeters using various image analysis techniques. Our approach consists of combining multiple quantitative descriptors to describe grain shape. This work is based on a comprehensive literature review across multiple scientific fields to extract numerous quantitative shape measurements. This paper focuses on roundness and roughness descriptors. A total of 25 descriptors, including Waddell roundness and fractal dimension, were extracted, compiled, and computed using Python. The descriptor computation code is provided as a library with this article. We use principal component analysis (PCA) to combine all descriptors in the same category without losing clarity and validated our approach on both generated and real grain images. For both roundness and roughness descriptors, the generated images and real grain images results are in accordance and could be summarized as follows. 1) The roundness descriptors PCA effectively distinguish grain shapes, performing comparably to form descriptors. However, it struggles to differentiate high degrees of roundness, and roughness significantly influences these results. 2) The roughness descriptors PCA excels at discriminating roughness intensity, despite the influence of form and roundness. These results align with our previous study on form descriptors and lead us to a new understanding of shape description: shape description includes both large-scale phenomena ("form") and small-scale phenomena ("roughness"). And roundness is a specific case of shape description where various shapes transition into a circle. This study highlights the potential of using PCA alongside image-based shape analysis to enhance the quantitative description of grains, offering valuable implications for volcanology, planetary sciences, petrology and other fields.