High Frequency Ultrasonic
Characterization of Carrot Tissue
Christopher Vick
Advisor: Dr. Navalgund Rao
Center for Imaging Science
Rochester Institute of Technology
Overview
•
•
•
•
•
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Introduction
Hypothesis
Theory
Experimental
Results
Conclusion
Introduction
• Ultrasound: fast, nondestructive,
noninvasive, and inexpensive.
• Long history of diagnostic use.
• Many medical applications consist of
interpreting an image, based on graylevel 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
Velocity (m/s), Attenuation
(1000 db/mm)
identifying a carrot’s exact texture difficult.
Velocity, Attenuation Vs. Cooking Time
1.4
1.2
1
0.8
0.6
0.4
0.2
0
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Cooking Time, Minutes
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.
Experimental Setup
Experimental
• Input Signal Selection:
• Input Signal FFT:
0
5
Frequency (MHz)
10
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 Readings
Source of Error: Magnitude Variation of same Carrot Readings
Relative Magnitude
1.20E-02
Series1
Series2
Series3
1.00E-02
8.00E-03
6.00E-03
4.00E-03
2.00E-03
0.00E+00
0
2000000
4000000
6000000
8000000
10000000
Frequency (Hz)
- Magnitude variation as high as 20%.
- Sources: Alignment, transducer coupling
Results: Normalized
Standard Deviation of Normalized Same Carrot Readings
Relative Magnitude
1.2
Series1
1
0.8
0.6
0.4
0.2
0
0
2000000
4000000
6000000
8000000
Frequency (Hz)
- Variance drops to below 7%.
10000000
Results: Different Segments
- Notice that Magnitude decreases as
the xylem core diameter increases.
Results: Normalized
- After Normalization, variation drops
significantly, to less than 10%
Results:Different Carrots
Source of Error: Different Carrots of Equal Cooking times
Relative Magnitude
8.00E-04
7.00E-04
6.00E-04
5.00E-04
4.00E-04
3.00E-04
2.00E-04
1.00E-04
0.00E+00
0
2000000
4000000
6000000
8000000
10000000
Frequency (Hz)
- Magnitude Variation can exceed 80%
- From alignment, coupling, natural sample differences
Results: Normalized
Standard Deviation of Different Normalized Carrots
1
Relative Magnitude
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
10 0 0 0 0 0
2000000
3000000
4000000
50 0 0 0 0 0
6000000
70 0 0 0 0 0
8000000
9000000
Frequency (Hz)
- 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.
Results: Normalized Response LUT
Results: Normalized Response LUT
Side View of
Normalized
Response LUT
0
5
Frequency (MHz)
10
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 Carrot Example
1) Given unknown
output signal
2) Program
calculates
signal FFT
Results: Unknown Analysis
Relative Magnitude
3) Program
normalizes
FFT, compares
to known FFTs.
Unknown sample system FFT, and FFT Match
1.2
Series1 Average, +-1 SD
1
0.8
0.6
0.4
0.2
0
0
1000000 2000000 3000000 4000000 5000000 6000000 7000000 8000000 9000000 1000000
0
Frequency (Hz)
4) Program identifies the best match.
5) Program Predicted time: 13 minutes
6) Actual Cooking time: 13 Minutes
Result: 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.
Special Thanks to:
Dr. Navalgund Rao
Maria Helguera
Brad Miller