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3D Image Quality Metrics for Porosity in Tissue Scaffolds
Craig Schroeder
Advisors:Ana Ivelisse Aviles - Statistical Eng. Div., ITL
Marcus Cicerone - ?? Div., MSEL
National Institute of Standards and Technology
August 11, 2005
Preliminaries
● Scaffold, tissue scaffold● Image (three-dimensional)● Porosity
– Volume of pore / total volume– 1 - density
Porosity Measurement
● Measure porosity– Scan in 3D scaffold
● MRI, CT scan
– Compute porosity
Image of MRI / CT equipment
Problem
● Inaccuracy in computed porosity– Scans contain noise– Noise affects computation– How accurate is the porosity?
Proposed Solution
● Develop an “Image Quality Metric”– Number computed for image– Higher quality indicates better accuracy
High Quality Low Quality
7 5 2 1
Metric Candidate One
● Find statistical relationships between– Computed porosity– Porosity after applying a filter– Actual porosity
● Estimate difference between– Computed porosity– Actual porosity
● “Image Quality Metric”
Stats
IQM
Original Noisy
Filtered
Statistical Relationships
Theory of Observed Slopes
● Clear trends in Least Squares angle● Explain trend with simple theory (Left)● Improve the theory for a better fit (Right)
Power Spectrum
● Signal processing● Amount of power broken down by frequency● Computed from Fourier transform
Metric Candidate Two
● Compute “power spectrum”● Find relationship between
– Computed porosity– Actual porosity– Power spectrum
● Use power spectrum to estimate error
Stats
IQM
Original Noisy
Power Spectrum
Noise Level vs Power Spectrum
● Need strong relationship between– Noise level (x axes)– Some aspect of the power spectrum (y axes)
Future Work
● Three weeks left!● Write this up as part of a publication● Consider permeability● Improve second metric
Image suggestsions?
Comments?
● Acknowledgments– Fredrick Phelan– Martin Chiang– NSF– NIST/ITL
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