High Frequency Ultrasonic Characterization of Carrot Tissue
Christopher VickAdvisor: Dr. Navalgund Rao
Center for Imaging Science
Rochester Institute of Technology
Introduction
• Ultrasound: fast, nondestructive, noninvasive, and inexpensive.
• Long history of diagnostic use.
• Many medical applications consist of interpreting an image, based on gray-level and texture.
Introduction
• System and processing limitations make this ineffective in identifying small variations in specific tissue structure.
• Computer texture analysis models are limited in scope.
• Models can be aided by quantitatively examining the ultrasonic response of tissue.
Alternate Ultrasound Uses
• Ripeness measurement in banana and avocado; animal backfat estimation; examination of the structure of metals and wood.
• Ultrasound has been proposed for texture evaluation of plant tissues, but not widely tested.
Why carrots?
• Biological changes well documented.
• Homogenous structure
• Since the changing carrot biology is well understood, can examine how ultrasound propagates through various tissues.
Previous Research Results:• Previous research used low frequency ultrasound.
• Notice the nature of their two variables. This makes
identifying a carrot’s exact texture difficult.
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Cooking Time, Minutes
Vel
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Velocity, Attenuation Vs. Cooking Time
Hypothesis
• High frequency ultrasound can be used to characterize the cell texture of cooked carrots.
• It is hypothesized that varied carrot tissues have uniquely identifiable frequency responses.
Ultrasound theory
• An ultrasound transducer can convert electrical energy to mechanical waves.
• Velocity and attenuation of this signal in a medium are characteristic of the medium’s physical properties.
• The amount of scattering, absorption, and reflection, are a function of the medium as well.
Transducer Response• Measure transducer response by filling the jar
setup with water.
- Less than 5% variation across response curve.
Carrot Sample Preparation• Samples were cored from
normal Dole carrots, using an apple corer.
• Samples to be cooked were placed in boiling water for the appropriate 0-16 minute cooking times, removed, and cooled in distilled water.
Tests: Same Sample
• Examine signal variation from imaging the same carrot sample, repeatedly.
- Align carrot/transducers
- Image the sample
- Remove the sample
- Repeat process
Testing: Different Samples
• Examine signal variation along the length of the carrot, as the xylem core diameter changes.
• Examine signal variation among different carrots of equal cooking time.
Testing: Cooked Carrots
• Random carrot segments, boiled for between 1-16 minutes, in 30 second intervals.
• Lastly, random carrot samples were cooked for an unknown length of time.
• If successful, results from the previous tests should allow for identification of the unknown samples.
Results: Same Sample ReadingsSource of Error: Magnitude Variation of same Carrot Readings
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2.00E-03
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Frequency (Hz)
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- Magnitude variation as high as 20%.
- Sources: Alignment, transducer coupling
Results: Normalized
- Variance drops to below 7%.
Standard Deviation of Normalized Same Carrot Readings
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Results:Different Carrots Source of Error: Different Carrots of Equal Cooking times
0.00E+00
1.00E-04
2.00E-04
3.00E-04
4.00E-04
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6.00E-04
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Frequency (Hz)
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- Magnitude Variation can exceed 80%
- From alignment, coupling, natural sample differences
Results: NormalizedStandard Deviation of Different Normalized Carrots
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Frequency (Hz)
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- Variation is significantly decreased.
- Is error too high to allow accurate classification?
Results: Various Cooked Carrots
- Frequency response changes can be explained by
the structural changes invoked through cooking.
Analysis: Unknown Sample
• IDL Program is given the system output signal of a carrot of unknown cooking time.
• Program calculates the FFT, normalizes it, and attempts to identify the lowest error associated with a match from the known LUT.
Results: Unknown Analysis3) Program
normalizes
FFT, compares
to known FFTs.
Unknown sample system FFT, and FFT Match
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Series1 Average, +-1 SD
4) Program identifies the best match. 5) Program Predicted time: 13 minutes
6) Actual Cooking time: 13 MinutesResult: Match
Only 10 unknown trial conducted. 4/10 successful.
Conclusions• Focused on the frequency response of
carrots.• Magnitude variation is important factor. • By normalizing, variation among same
sample, or different segments is lowered substantially.
• Large signal variation among different carrots.
Conclusion:
• IDL analysis needs further attention; not all carrots can be identified.
• Combining analysis with the previously studies variables of Velocity and Attenuation would likely provide a more robust tissue identification model.