From rocks to pixels: a comprehensive framework for grain shape characterization through the image analysis of size, orientation, and form descriptors

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 form descriptors on each other, which explains the challenges in identifying the best descriptors. This work demonstrates the potential of combining PCA with image-based shape analysis to improve the quantitative description of grains, with implications for sedimentology, mineral exploration, and other fields.