Unsupervised texture classification of 3D X-ray Micro-computed Tomography images

Tamara A.I. Almeghari, Mohamed Soufiane Jouini, Fawaz Hjouj

    Research output: Contribution to journalConference articlepeer-review


    Characterizing rock proprieties is crucial in the oilfield to evaluate hydrocarbon reserves. Several studies showed a high correlation between rock properties and textures. Therefore, we propose integrating texture information in the images to identify precisely the most representative textures in highly heterogeneous rocks to estimate their properties. First, we implemented a steerable pyramid decomposition to extract the texture features. Then, those parameters were used as input for the Self-organizing map to classify the textures. Finally, by applying several models and comparing their results, we suggested the best approach to implement for texture classification.

    Original languageBritish English
    Article number012143
    JournalJournal of Physics: Conference Series
    Issue number1
    StatePublished - 2024
    Event12th International Conference on Mathematical Modeling in Physical Sciences, IC-MSQUARE 2023 - Belgrade, Serbia
    Duration: 28 Aug 202331 Aug 2023


    Dive into the research topics of 'Unsupervised texture classification of 3D X-ray Micro-computed Tomography images'. Together they form a unique fingerprint.

    Cite this