1. Automated OTMS
Raghav Malik

2. Background Tissue thickness measurements Several methods already exist
Biomechanics applications
Dealing with soft tissue
Useful for determining tensile strength
Applications in Finite Element Analysis
Several methods already exist
Calipers/Thickness Gauge
LVDT Probe
Electrical Resistance Gauge

3. Challenges/Motivations
Caliper/Thickness Gauge
Easy to setup and use
Requires physical contact
Induces deformation
Artificially low measurements
Electrical Resistance Gauge
No contact deformation
Sensitive to temperature
Requires extensive calibration
Expensive
LVDT Probes and Laser Displacement Probes
High precision
Extremely sensitive to temperature
Relies on voltage measurements
Artificially high readings

4. Premise of AutoOTMS Works on high resolution medical images of tissue
No contact so no deformation
Precision bounded only by resolution of image
Computation carried out by software
No inconsistency associated with human application
Streamlines the data collection process
Quick and inexpensive
Deals entirely with physical units
No extra calibration steps necessary
Insensitive to nature of the tissue
Operates separately from the tissue
Insensitive to environmental factors

5. Specifications Image to be analyzed Tissue sample
Caliber of known thickness in the same plane as tissue
Placed on high-contrast table (easily identify bottom edge)
Display average pixel heights of tissue and caliber
Display standard deviation of pixel heights
Display actual average thickness of tissue (based on proportion)
Option to output analysis results to an XML file with marked up image

6. Edge Detection Analyze column by column
Reduce 3D RGB signal to 1D intensity signal
Transform into differential domain
"High" spike in derivative = edge
Relative image clustering

7. Edge Detection Visualization

8. Error Detection Need to remove false edges from image
Edge with error
Need to remove false edges from image
Transform detected edge into derivative
Detect spikes (p < 0.01)
Spikes come in pairs:
Cancel positive and negative
Accumulate the net
Accumulate the derivative
Error removed from edge
Derivative of edge
Spike detected and removed

9. Sample Output

10. XML Caching Save the results of a scan Easy to read Provide overview
No need to repeat the scan
Format of XML
Image file information
Calibration units
Scan information
Statistics
Image files
Save original image
Save marked up image

11. Complete Workflow Start program Python 3.x Run graphics.py script
Load image
From file
From camera
Specify parameters
Left and right of tissue
Left and right of caliber
Calibrate
Thickness of caliber in physical units
Review Statistics
Average pixels
Average mm
Standard Deviation
Save results
Cache scans to XML file
Save original and marked up images

12. Technologies Self-contained python script Cross-platform compatible
Good for scientific measurements
Maintains open-source paradigm
Relies on widely used python libraries
Tkinter for graphics: native
NumPy for numerical manipulation
PIL for image manipulation
Basic XML writer to "cache" the results
Store original and marked up image
Information about scan locations and frequency: reproducible

13. Results Sample Name AutoOTMS Trial 1 AutoOTMS Trial 2 AutoOTMS Trial 3
Manual OTMS
oks00TR1-FMC-MCXX
2.030 mm
2.027 mm
2.028 mm
1.952 mm
oks00TR1-MNS-LCuX
1.013 mm
0.996 mm
1.088 mm
0.854 mm
Sample Name
AutoOTMS Mean
Manual OTMS Mean
Difference
% Error
oxs00TR1-FMC-MCXX mm
1.952 mm
0.076 mm
3.9%
oks00TR1-MNS-LCuX mm
0.854 mm
0.178 mm
20.1 %
Sample Name
AutoOTMS Stdev
AutoOTMS CV
Manual Stdev
Manual CV
CV Ratio
FMC-MCXX mm mm
58.23x
MNS-LCuX mm mm
1.46x

14. Conclusions Delivers consistent results
AutoOTMS Standard Deviations lower than Manual OTMS Standard Deviations
Coefficient of Variation smaller than 1 for both tissues
AutoOTMS Coefficient of Variation lower than Manual OTMS Coefficient of Variation
oks00TR1-FMC-MCXX
AutoOTMS very close to Manual OTMS results (3.9% difference)
Slightly higher
oks00TR1-MNS-LCuX
AutoOTMS differs significantly from Manual OTMS results (20.1% difference)
Significantly higher
Conclusion: Results are precise; accuracy should be verified by a third method of measurement

15. Acknowledgements Cleveland Clinic
Dr. Erdemir and the rest of the Open Knee(s) team
The Open Knee(s) project
Mentor High School