2011-2012 Apatite to Zircon Computer Science Clinic Automated

2011-2012 Apatite to Zircon Computer Science Clinic Automated

2011-2012 Apatite to Zircon Computer Science Clinic

Automated Characterization of Fission
Tracks in Apatite Crystals
Apatite to Zircon, Inc.

One tip of a fission track

Tip Finding

An etch pit, 1.5 microns long

Apatite to Zircon, Inc. specializes in geological dating by
apatite and zircon fission track methods. The company has
four employees and is located in Viola, Idaho. Companies
and universities interested in learning about the geological
history of a rock bed can send samples to A2Z for analysis.

contour with
parabolic fit



An apatite crystal

Apatite (Ca10(PO4)6[F,Cl,OH,Br]2) is a
common mineral compound in both
rocks and organisms. Uranium-238
occurs naturally in apatite and has a
half-life of 4.468 billion years. When
fission events occur in an apatite
crystal, a linear track is created.
These tracks can be revealed under a
microscope following a polishing and
acid etching process, and the track
characteristics can tell a lot about the
crystals history. The etching process
produces small features called etch
pits. Their size and orientation can be
used to infer information about the
grain composition.

One of the most important
applications of fission track dating
is its role in informing oil
exploration and mining operations.
However, obtaining any useful
information requires many tedious
hours of repetitious work in which a
geologist locates and measures
fission tracks and etch pits. Even
automating the simplest portions of
these tasks can result in large
time-savings, and the geologists
time can be spent making
decisions that require expertise
and judgment.

Wrong Tips

Poor contrast score
Contrast: 0.048
Shape: 0.97

A grain of Apatite (image is 87x66 microns)

Track Finding
Blue lines come
from the Hough
Blue dots are tip
hint locations
Red dots are
potential tips

Problem Statement
The goal of this project is to build an application for finding
and characterizing fission tracks and fission track tips in
apatite grains as autonomously as possible, with the ability for
a human user to correct and identify those features as
needed or desired. The application will also include a library
of results.

Good tip scores
Contrast: 0.27
Shape: 0.98

Correct Tip


A geologist identifying
fission tracks

Data Library

The green line is
the resulting track

Tracks are found as follows:
1. Extract straight lines using the
Hough transform
2. Extend these lines to determine tip candidates
3. Consider all pairs of found tips on either side of
each line
4. Select the highest scoring pair
Tracks are scored based on:
edge strength along the line
the contrast and shape scores of its tips
Angle agreement among the tips and the line

Poor shape score
Contrast: 0.18
Shape: 0.10

The library serves as a database of all the information the
user and the program have identified. Items in the library
can be edited in case the user or automatic routines make a
mistake, and the entire data set can be imported into
Microsoft Excel or other programs for further analysis.

User Interface and Deliverables

Tips can be located automatically near a hint location.
The algorithm works as follows:
1. Threshold to extract foreground features
2. Extract smooth contours around features
3. Find point of highest curvature
4. Fit a parabola to the contour
5. Score based on shape and contrast

Etch Pit Analysis
Analyzing the etch pits in a grain provides important information
to the geologist about the apatite crystal itself. Etch pits are
detected by analyzing the entire image as follows:
1. Threshold to extract foreground features
2. Smooth the contours
3. Rule out some contours based on size
4. Fit ellipses and rule out those with poor ellipse fit
5. Choose the largest population of features whose
ellipses are oriented at a similar angle
Results from
automatic etch
pit analysis:
Red features
are potential
etch pits
Green features
are the best
selected etch

The team is delivering its system to A2Z with a user
interface that encapsulates all of its automatic and humanassistive functionality. The interface can load a stack of
images, scroll through them, and interact with the image
features using the mouse. Menus and buttons provide
access to automated tip-, track-, and etch-pit-finding.

Team Members
Leif Gaebler 12
Calvin Loncaric 12 (PM)
Colin OByrne 12
Thea Osinski 12

A2Z Liaison
Ray Donelick, Ph.D.
Faculty Advisor
Zachary Dodds

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