ZEISS Maximum Birefringence Projector

From the Solutions Lab for mineralogy using light microscopes

When performing mineral classification for automated petrographic light microscopy data, the mineral crystals are typically randomly oriented relative to the direction of polarization. Complete information can be found by acquiring crossed polarized data at multiple relative orientations of polarizer/analyzer pair to the sample. This data is not optimally presented to allow for a machine learning algorithm to construct an accurate classification algorithm as the color, intensity and texture information that encodes mineral information is spread across all the different polarization orientations, presented as channels of the image. A maximal birefringence representation of the data, where each pixel is assigned the maximum intensity birefringence value from all crossed-polarized angles, allows the machine learning algorithm to directly lock into characteristic mineral signals.

ZEISS Maximum Birefringence Projector takes multi-polarized automated petrography data and re-constitutes it into a maximum-birefringence representation, with a channel represented in Red-Green-Blue (RGB) color space, and one in Hue-Saturation-Value (HSV) color space. As a change in polarization representation potentially engenders an image shift, a polarization channel registration utility is included to minimize the risk of image blurring. The re-ordered data is then saved in a format which transmits all the sample metadata in a format fully readable by ZEN microscopy image analysis software. This format is perfect to then be taken forward into subsequent mineral classification and automated analysis.

When performing mineral classification for automated petrographic light microscopy data, the mineral crystals are typically randomly oriented relative to the direction of polarization. Complete information can be found by acquiring crossed polarized data at multiple relative orientations of polarizer/analyzer pair to the sample. This data is not optimally presented to allow for a machine learning algorithm to construct an accurate classification algorithm as the color, intensity and texture information that encodes mineral information is spread across all the different polarization orientations, presented as channels of the image. A maximal birefringence representation of the data, where each pixel is assigned the maximum intensity birefringence value from all crossed-polarized angles, allows the machine learning algorithm to directly lock into characteristic mineral signals.

ZEISS Maximum Birefringence Projector takes multi-polarized automated petrography data and re-constitutes it into a maximum-birefringence representation, with a channel represented in Red-Green-Blue (RGB) color space, and one in Hue-Saturation-Value (HSV) color space. As a change in polarization representation potentially engenders an image shift, a polarization channel registration utility is included to minimize the risk of image blurring. The re-ordered data is then saved in a format which transmits all the sample metadata in a format fully readable by ZEN microscopy image analysis software. This format is perfect to then be taken forward into subsequent mineral classification and automated analysis.

Highlights

Area of Research:

  • Natural Resources

Sample Types:

  • Crystals with mineralization

Related Solutions:

  • Thin section permeability prediction
Maximum Birefringence Projector cross-pol pair  image 1 © Scan of crossed-polarized light microscope, showing a random orientation of optical indicatrix with the optical axis. If the relative orientation of the polarizer/analyzer pair and the sample is rotated, maximal birefringence information can be extracted.
Scan of crossed-polarized light microscope, showing a random orientation of optical indicatrix with the optical axis. If the relative orientation of the polarizer/analyzer pair and the sample is rotated, maximal birefringence information can be extracted.
Maximum Birefringence Projector cross-pol pair  image 2
Maximal birefringence representation of the light microscopy data ideally represented for automated classification using machine learning.

Required components

  • ZEISS Axio Scan
  • ZEN
  • ZEISS Maximum Birefringence Projector
  • ZEN Intellesis
  • ZEN Image Analysis

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