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Minerals
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Applications of Microscopy Image Processing and Machine Learning in Thin...
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Applications of Microscopy Image Processing and Machine Learning in Thin Sections

A special issue of Minerals (ISSN 2075-163X). This special issue belongs to the section "Mineral Exploration Methods and Applications".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 9658

Special Issue Editors

Dr. Miguel Ángel Caja

E-Mail Website
Guest Editor
Repsol Technology Lab, Agustín de Betancourt, s/n. 28935 Móstoles, Madrid, Spain
Interests: petrography; diagenesis; image analysis; cuttings; digital rock physics
Dr. Ardiansyah Koeshidayatullah

E-Mail Website
Guest Editor
1. Department of Geosciences, College of Petroleum Engineering & Geosciences, Building 76, KFUPM, Dhahran 31261, Saudi Arabia
2. Department of Geological Sciences, Stanford University, Stanford, CA 94305, USA
Interests: sedimentology; carbonate petrography; image processing; computer vision; deep learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chandra L. Reedy

E-Mail Website
Guest Editor
Center for Historic Architecture & Design, University of Delaware, 331 Alison Hall, 240 Academy Street, Newark, DE19716, USA
Interests: thin section petrography of cultural materials; ceramic petrography; micro-CT, 2D and 3D image analysis of ceramic

Special Issue Information

Dear Colleagues,

It is surprising how a thin slice of a rock or mineral sample prepared in a laboratory, known as thin section or petrographic thin section, can be used with different microscopic techniques such as polarizing petrography, electron microscopy, electron microprobe, cathodoluminescence, Raman spectroscopy, micro-X-ray fluorescence, etc. This versatility of analytical techniques makes thin sections applicable and useful for a variety of interests:

  • Sedimentary, igneous, and metamorphic petrography;
  • Oil and gas exploration and production reservoir characterization;
  • Digital rock physics;
  • Cultural materials and conservation research;
  • Mineral deposits exploration;
  • Concrete analysis;
  • And many others.

Over the last few years, the rapid spread of digital transformation and development of technologies has provided a variety of image processing techniques, open-source codes, and easy and cheap access to computing power through virtual machines. This is further powered by the availability of diverse Machine Learning algorithms generating a lot of interest in researchers from different fields working with thin sections and digital images, allowing them to extract meaningful information. 

This Special Issue is dedicated to new insights into image processing and machine learning in thin sections and their applications in different fields and industries.

Dr. Miguel Ángel Caja
Dr. Ardiansyah Koeshidayatullah
Prof. Dr. Chandra L. Reedy
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Minerals is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • thin sections
  • image processing
  • machine learning
  • computer vision
  • petrography
  • rocks

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Published Papers (2 papers)

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Research

22 pages, 14568 KiB  
Article
Digital Rock Physics in Cuttings Using High-Resolution Thin Section Scan Images
by Miguel Ángel Caja, José Nicolás Castillo, Carlos Alberto Santos, José Luis Pérez-Jiménez, Pedro Ramón Fernández-Díaz, Vanesa Blázquez, Sergi Esteve, José Rafael Campos, Telm Bover-Arnal and Juan Diego Martín-Martín
Minerals 2023, 13(9), 1140; https://doi.org/10.3390/min13091140 - 29 Aug 2023
Cited by 1 | Viewed by 1947
Abstract
Digital rock physics (DRP) has undergone significant advancements in the use of various imaging techniques to acquire three-dimensional volumes and images of rock samples for the computation of petrophysical properties. This study focuses on developing a DRP workflow using high-resolution thin section scans [...] Read more.
Digital rock physics (DRP) has undergone significant advancements in the use of various imaging techniques to acquire three-dimensional volumes and images of rock samples for the computation of petrophysical properties. This study focuses on developing a DRP workflow using high-resolution thin section scans for computing porosity and permeability in cuttings samples. The workflow was tested on quarry sandstone plug samples and artificially generated pseudo-cuttings before applying it to real cuttings from oil and gas wells. The results show that the porosity and permeability values obtained through the DRP workflow are statistically equivalent to those obtained through conventional routine core analysis (RCAL). The workflow was also able to handle the presence of various lithologies in real cuttings samples. The study demonstrates the feasibility of obtaining porosity and permeability values in cutting samples using the DRP approach, offering a fast and cost-effective methodology that provides additional data and allows linking petrophysical properties to image data from the cuttings. Full article
(This article belongs to the Special Issue Applications of Microscopy Image Processing and Machine Learning in Thin Sections)
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37 pages, 34345 KiB  
Article
Petrographic Microscopy with Ray Tracing and Segmentation from Multi-Angle Polarisation Whole-Slide Images
by Marco Andres Acevedo Zamora and Balz Samuel Kamber
Minerals 2023, 13(2), 156; https://doi.org/10.3390/min13020156 - 20 Jan 2023
Cited by 8 | Viewed by 6358
Abstract
‘Slide scanners’ are rapid optical microscopes equipped with automated and accurate x-y travel stages with virtual z-motion that cannot be rotated. In biomedical microscopic imaging, they are widely deployed to generate whole-slide images (WSI) of tissue samples in various modes of illumination. The [...] Read more.
‘Slide scanners’ are rapid optical microscopes equipped with automated and accurate x-y travel stages with virtual z-motion that cannot be rotated. In biomedical microscopic imaging, they are widely deployed to generate whole-slide images (WSI) of tissue samples in various modes of illumination. The availability of WSI has motivated the development of instrument-agnostic advanced image analysis software, helping drug development, pathology, and many other areas of research. Slide scanners are now being modified to enable polarised petrographic microscopy by simulating stage rotation with the acquisition of multiple rotation angles of the polariser–analyser pair for observing randomly oriented anisotropic materials. Here we report on the calibration strategy of one repurposed slide scanner and describe a pilot image analysis pipeline designed to introduce the wider audience to the complexity of performing computer-assisted feature recognition on mineral groups. The repurposed biological scanner produces transmitted light plane- and cross-polarised (TL-PPL and XPL) and unpolarised reflected light (RL) WSI from polished thin sections or slim epoxy mounts at various magnifications, yielding pixel dimensions from ca. 2.7 × 2.7 to 0.14 × 0.14 µm. A data tree of 14 WSI is regularly obtained, containing two RL and six of each PPL and XPL WSI (at 18° rotation increments). This pyramidal image stack is stitched and built into a local server database simultaneously with acquisition. The pyramids (multi-resolution ‘cubes’) can be viewed with freeware locally deployed for teaching petrography and collaborative research. The main progress reported here concerns image analysis with a pilot open-source software pipeline enabling semantic segmentation on petrographic imagery. For this purpose, all WSI are post-processed and aligned to a ‘fixed’ reflective surface (RL), and the PPL and XPL stacks are then summarised in one image, each with ray tracing that describes visible light reflection, absorption, and O- and E-wave interference phenomena. The maximum red-green-blue values were found to best overcome the limitation of refractive index anisotropy for segmentation based on pixel-neighbouring feature maps. This strongly reduces the variation in dichroism in PPL and interference colour in XPL. The synthetic ray trace WSI is then combined with one RL to estimate modal mineralogy with multi-scale algorithms originally designed for object-based cell segmentation in pathological tissues. This requires generating a small number of polygonal expert annotations that inform a training dataset, enabling on-the-fly machine learning classification into mineral classes. The accuracy of the approach was tested by comparison with modal mineralogy obtained by energy-dispersive spectroscopy scanning electron microscopy (SEM-EDX) for a suite of rocks of simple mineralogy (granulites and peridotite). The strengths and limitations of the pixel-based classification approach are described, and phenomena from sample preparation imperfections to semantic segmentation artefacts around fine-grained minerals and/or of indiscriminate optical properties are discussed. Finally, we provide an outlook on image analysis strategies that will improve the status quo by using the first-pass mineralogy identification from optical WSI to generate a location grid to obtain targeted chemical data (e.g., by SEM-EDX) and by considering the rock texture. Full article
(This article belongs to the Special Issue Applications of Microscopy Image Processing and Machine Learning in Thin Sections)
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