High Frequency Ultrasonic Characterization of Carrot Tissue

Christopher VickAdvisor: Dr. Navalgund Rao

Center for Imaging Science

Rochester Institute of Technology

Overview

• 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 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.

00.20.40.60.8

11.21.4

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Cooking Time, Minutes

Vel

oci

ty (

m/s

), A

tten

ua

tio

n

(10

00

db

/mm

)

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.

Experimental Setup

Experimental• Input Signal Selection:

• Input Signal FFT:

0 5 10Frequency (MHz)

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

0.00E+00

2.00E-03

4.00E-03

6.00E-03

8.00E-03

1.00E-02

1.20E-02

0 2000000 4000000 6000000 8000000 10000000

Frequency (Hz)

Rel

ativ

e M

agni

tude

Series1Series2Series3

- Magnitude variation as high as 20%.

- Sources: Alignment, transducer coupling

Results: Normalized

- Variance drops to below 7%.

Standard Deviation of Normalized Same Carrot Readings

0

0.2

0.4

0.6

0.8

1

1.2

0 2000000 4000000 6000000 8000000 10000000

Frequency (Hz)

Rel

ativ

e M

agni

tude

Series1

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

0.00E+00

1.00E-04

2.00E-04

3.00E-04

4.00E-04

5.00E-04

6.00E-04

7.00E-04

8.00E-04

0 2000000 4000000 6000000 8000000 10000000

Frequency (Hz)

Rel

ativ

e M

agni

tude

- Magnitude Variation can exceed 80%

- From alignment, coupling, natural sample differences

Results: NormalizedStandard Deviation of Different Normalized Carrots

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 1000000 2000000 3000000 4000000 5000000 6000000 7000000 8000000 9000000

Frequency (Hz)

Rel

ativ

e M

agni

tude

- 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 10

Frequency (MHz)

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 Analysis3) Program

normalizes

FFT, compares

to known FFTs.

Unknown sample system FFT, and FFT Match

0

0.2

0.4

0.6

0.8

1

1.2

0 1000000 2000000 3000000 4000000 5000000 6000000 7000000 8000000 9000000 1000000

0

Frequency (Hz)

Rel

ativ

e M

agni

tude

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.

Special Thanks to:Dr. Navalgund Rao

Maria Helguera

Brad Miller