3D and 2D Bubbles In Rock

Use Computer Vision and Machine Learning to Discover and Segment Chondrules in Meteorites

Hackathon Findings

• tk

Hackathon Projects

• Frame-seeker and its presentation
• Meteoright and its presentation
• Chondrule Segmentation 3d unet and its presentation
• 3d and 2d Bubbles In Rock Overnight Owls and its presentation

Background

Abstract

Many meteorites record the early accretion of small solids into larger rocks. So do the planets, but we can study the process better in meteorites. Many have small (<1 mm) spherical rocks called chondrules (not "bubbles") with fine grained mineral dust matrix in between. We image small chunks of meteorites in 3D using synchrotron computed tomography (CT), and then we slice them with saws to make polished surfaces we can map in 2D. From the 3D and 2D data we try to measure the sizes of the chondrules in the meteorite by segmenting (outlining) them. Once they are segmented, we can get more information about each object (see Mineral Mapping challenge). Until now, the only way to segment the objects has been by hand (ugh!). Can we segment the roundish chondrules in Semarkona automatically? In CT, we have 3D data but not much chemical data. In 2D we have chemical information. Here is a map of the elements Mg, Ca, Fe element x-ray maps in red-green-blue composite:

Data

The CT data are in tiff stacks where each tiff is a consecutive slice through the rock. Here is the raw rock with a flat face on the left size in the Y-Z plane (Z is up), and two frames of CT data parallel to Y-Z:

A movie in the Y-Z direction is here: http://research.amnh.org/~debel/meteorites/Semarkona/SEm2_YZ_15fps.avi

The 2D chemical maps record the chemical abundances of elements in each pixel, and are combined into 3-element red-green-blue composites, like this Mg-Ca-Al map:

We also collect maps of back-scattered electrons (BSE). Electrons "bounce" more readily off denser material, like iron-rich grains. Here is a BSE map of sample 1:

Challenges

How would you recognize The round things, frozen magnesium-silicate rocks called chondrules, with computer vision?

a: From 3D CT data?

b: From 2D element intensity maps?

Sub-Challenge

Left: YZ plane #171 in CT stack, Right: BSE image of cut face 1B.

Sub challenge: Find the frame in the tomography - what plane in the full tomography volume best matches the BSE map of a flat polished surface cut from the original rock?

Methods: Detail

Synchrotron computed tomography (CT) was done at beamline 13BM at the Advanced Photon Source, Argonne National Lab, Illinois, a Department of Energy user facility.

CT yields interpretable tiff stacks of 3D density structure. Each volume element (voxel) has a value for its x-ray attenuation. Each cubic voxel is 17 micron on each edge. These data were collected in 12-bit, rendered into 16-bit for reconstruction (the "computed" part), and output as either 8- or 16-bit tiff stacks. A ~1 cubic cm. chunk of the meteorite called Semarkona (LL3.0 type) is the target for segmentation: http://research.amnh.org/~debel/meteorites/Semarkona/Sem2_YZ_8a/ (8-bit tiff stack in Y-Z plane) http://research.amnh.org/~debel/meteorites/Semarkona/Sem2_Z_16a/ (16-bit tiff stack in Z plane) The chondrules are the round things. This is a grayscale image analysis problem we approached in 2004 (http://adsabs.harvard.edu/abs/2004M%26PSA..39.5153E) using an easier meteorite to segment.

X-ray intensity maps were made with wavelength dispersive spectrometers (WDS) and an energy dispersive spectrometer (EDS) on the Cameca SX100 electron probe microanalyzer (EPMA) at the AMNH. The EPMA is a kind of electron microscope, where the electron gun makes an about 1 micron beam on the flat sample surface, and each element there emits x-rays of characteristic wavelength. The x-ray intensity at an element's wavelength at each pixel in a raster is directly proportional to the weight fraction of each element in that pixel. Chemical maps record the chemical abundances of elements at each 1 micron pixel, where pixels are 8 microns apart for this project. Raster maps 512 x 512 pixels in size (8-bit depth) are stitched together and made into color-balanced red-gree-blue (RGB) composite mosaics. The round things are frozen magnesium-silicate rocks called "chondrules" - how would you recognize one with computer vision?

Solutions

Here is an image of sample 1, outlined by Ana Lobo (yellow lines).

Resources

Be sure to check the Online Resources and Data Sets page to see if there might be any general purpose code or utilities you might use, especially for computer vision and image processing.

• Challenge data files on Google Drive: A few publicly available files (unprocessed images, processed images, Adobe Illustrator files, etc.) for the Semarkona meteorite piece are available in this repository.

**Please note: although the files for this challenge are not yet published, we do not mind if you keep or distribute these files after the hackathon is over. As with all challenges, there will be opportunities for those interested in continuing to work on their projects after the hackathon! **

• Papers on the subject: You can find two papers in the challenge directory here on GitHub.

2D image processing:

• ImageJ2: We think that there are tools in ImageJ (Fiji) that might be useful for some of this work.
• FIJI - Fiji Is Just ImageJ: This version of ImageJ may be easier to get started with, but may also not have the same capabilities as the full toolkit.
• CellProfiler: This toolkit may have some capabilities of identifying structures within cells or otherwise contributing to 2D image processing, and may be adaptable to this challenge.

3D image manipulation:

• Point Cloud Library: PCL has a bunch of tools to deal with 3D point cloud data and might be used to match or segment objects. See: http://docs.pointclouds.org/trunk/group__registration.html

Maya:

• Maya scripting documentation: Official documentation that explains Maya MEL scripting language and how to use Python in Maya scripting
• SimplyMaya scripts: Example MEL scripts from SimplyMaya

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Challenge owner: Denton Ebel