An image analysis method to determine crystal size distributions of olivine in kimberlite

Eun-Jung Holden, S. Moss, J.K. Russell, Mike Dentith

    Research output: Contribution to journalArticlepeer-review

    21 Citations (Scopus)


    Crystal size distributions (CSDs) are a standard method of describing populations of crystals within magmatic rocks. Olivine is the dominant phase in kimberlite (∼40–50% by volume) and features a diverse range of sizes, shapes and origins. CSDs of olivine provide a logical means of semi-quantitatively characterising kimberlite. The CSDs can then be used to distinguish or correlate between kimberlite bodies or to investigate processes related to ascent, emplacement and eruption. In this paper, we present an automatic image analysis technique that provides efficient quantification of olivine CSDs within digital images of polished slabs of kimberlite. This technique relies on a combination of algorithms for detecting regions of interest (ROI) and for segmentation of ROIs in order to identify individual olivine crystals that are used for size distribution datasets. The detection process identifies regions expected to be olivine using a model-based colour detection technique using Mahalanobis distance combined with texture analysis based on local standard deviation and greyscale foreground enhancement techniques. The segmentation process separates adjacent domains to identify individual crystals using an iterative marker-based watershed algorithm to separate adjoined structures of varying sizes. We demonstrate the utility of automatic image analysis by comparing CSDs for olivine derived from this method versus results from manual digitisation of olivine grains. The automatic detection system correctly identified ∼86% of the manually detected olivine domains; ∼88% of the automatically detected regions correctly correlate to manually defined olivine grains. Discrepancies between the two methods are mostly the result of oversimplification of crystal margins (i.e. rounding) by manual tracing whereas automatic boundary recognition shows clear advantages in identifying irregularities in crystal edges. Closer examination of the results shows that both methods suffer from under-representation of smaller crystals due to: (1) human subjectivity and error in manual tracing and (2) noise removal processes in automatic detection. Automatic detection of olivine grains is much more efficient than conventional manual tracing; manual detection requires ∼6 h per sample versus ∼1 min for automatic analysis of the same sample.
    Original languageEnglish
    Pages (from-to)255-268
    Issue number3
    Publication statusPublished - 2009


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