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Author Spotlight: Non-Invasive Imaging of Complex Bio-Structures Using Polarization-Sensitive Two-Photon Microscopy

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A multiangle polarised imaging-based method for thin section segmentation.

Yan Chen^1,2, Yu Yi¹, Yongfang Dai³

¹School of Computer Science, Southwest Petroleum University, Chengdu, Sichuan, China.

Journal of Microscopy

|January 15, 2024

Summary

This summary is machine-generated.

This study presents an advanced image processing technique for segmenting sandstone thin sections, improving mineral particle identification in petroleum geology. The method enhances accuracy and efficiency for exploring underground oil and gas reservoirs.

Keywords:

concave point extinction characteristics image segmentation thin section identification

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Last Updated: Jul 5, 2025

Author Spotlight: Non-Invasive Imaging of Complex Bio-Structures Using Polarization-Sensitive Two-Photon Microscopy

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Published on: September 8, 2023

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Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes

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Published on: March 20, 2018

Area of Science:

Petroleum Geology
Image Processing
Mineralogy

Background:

Petroleum geology relies on identifying underground oil and gas reservoirs.
Rock thin section analysis, specifically particle segmentation, is crucial for this identification.
Traditional methods struggle with complex sandstone thin sections due to blurred boundaries and low porosity.

Purpose of the Study:

To develop an improved image segmentation method for sandstone thin sections.
To address limitations of traditional methods in complex lithologies.
To enhance the accuracy and efficiency of thin section identification.

Main Methods:

Combines petrology and image processing techniques.
Image alignment and transformation to HSV color space for pore extraction.
Particle extraction using extinction characteristics.
Concavity and corner detection matching for particle processing.

Main Results:

The proposed method achieves more accurate fitting of mineral particle boundaries compared to existing techniques.
Demonstrates excellent performance in actual production scenarios.
Significantly improves thin section identification efficiency.

Conclusions:

The developed method effectively segments complex sandstone thin section images.
It offers a valuable tool for petroleum geologists, enhancing reservoir exploration.
The technique improves expert identification efficiency and accuracy.