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Medical Image ProcessingMedical Image Processing

By:By: AmitAmit ChoudharyChoudhary

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ProblemProblem The specific problem this project deals with is findingThe specific problem this project deals with is finding

nerve clusters in an ultrasound image.nerve clusters in an ultrasound image.

Variation of patient anatomies may create difficulties forVariation of patient anatomies may create difficulties forthe clinician when trying to locate nerves and nervethe clinician when trying to locate nerves and nerveclusters.clusters.

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PurposePurpose

Create software that accurately identifies nerves in anCreate software that accurately identifies nerves in an

ultrasound image.ultrasound image.

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Dealing with TextureDealing with Texture

To create software that identifies nerves, we assume thatTo create software that identifies nerves, we assume that

nerves have a unique texture.nerves have a unique texture.

To the untrained eye, an ultrasound image has very fewTo the untrained eye, an ultrasound image has very few

discernable features.discernable features. Using MatLab, we can essentially zoom in on an imageUsing MatLab, we can essentially zoom in on an image

and look at the grayscale value of every pixel to see ifand look at the grayscale value of every pixel to see if

there are patterns that arise from the Regionsthere are patterns that arise from the Regions--ofof--Interest.Interest.

RegionsRegions--ofof--InterestInterest ororROIROI are the areas that a clinicianare the areas that a clinicianidentifies as nerve clusters.identifies as nerve clusters.

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ProcessProcess

1.1. Collect Samples and get clinicians to identify ROICollect Samples and get clinicians to identify ROI

2.2. Extract ROI using MatLabExtract ROI using MatLab

3.3. Break ROI into smaller patchesBreak ROI into smaller patches

4.4. Find statistical measures of patches that will define theFind statistical measures of patches that will define the

texture of nerve clusterstexture of nerve clusters

5.5. Detect nerve clusters in unmarked images by breakingDetect nerve clusters in unmarked images by breaking

up an entire unmarked image into patches, find theup an entire unmarked image into patches, find the

statistical measures of those patches, and mark the onesstatistical measures of those patches, and mark the onesthat resemble the patches from our ROIs.that resemble the patches from our ROIs.

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ProcessProcess

1.1. Collect samples and have an expert clinician manuallyCollect samples and have an expert clinician manually

identify the nerve clusters, or ROI.identify the nerve clusters, or ROI.

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ProcessProcess

2.2. Extraction of RegionExtraction of Region--ofof--Interest.Interest.

How does MatLab process images?How does MatLab process images?

There is a command in MatLab There is a command in MatLab imread(NameOfImage)imread(NameOfImage) that allows that allowsus to break down an entire image into an (m x n x 3) matrix, where mus to break down an entire image into an (m x n x 3) matrix, where mand n is the resolution of the image. The 3and n is the resolution of the image. The 3rdrd dimension representsdimension representsthe three RGB values. ( Red / Green / Blue )the three RGB values. ( Red / Green / Blue )

In this case, the image we are looking at is calledIn this case, the image we are looking at is called20060726_orbach6_ax1.tiff

and is a 379 by 476 pixel image.379 by 476 pixel image.

So if we typeSo if we typeImg=imread('20060726_orbach6_ax1.tiff');

into MatLab, we get a 379 by 476 by 3 matrix called Img, where therange of the values are from 0 to 255.

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ProcessProcess2.2. Extraction of RegionsExtraction of Regions--ofof--Interest.Interest.

a)a) Get the contour.Get the contour.

b)b) Insert Seeds.Insert Seeds.

c)c) Dilate the seeds repeatedly, negating the parts of the new dilationDilate the seeds repeatedly, negating the parts of the new dilationthat overlap with the contour. Repeat dilations until the lastthat overlap with the contour. Repeat dilations until the last

dilation is the same as the previous dilation.dilation is the same as the previous dilation.

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a)a) TheThe ContourContour is the boundary of the ROI.is the boundary of the ROI.

In the Img, the ROI is marked by a very specific yellow. TheIn the Img, the ROI is marked by a very specific yellow. TheRGB value of this yellow is (251, 225, 1). To create the Contour,RGB value of this yellow is (251, 225, 1). To create the Contour,we make a new zerowe make a new zero--matrix the same size as Img, (379,476).matrix the same size as Img, (379,476).We then make an If statement that places a 1 in all the matrixWe then make an If statement that places a 1 in all the matrix

entries that have RGB values equal to (251, 225, 1).entries that have RGB values equal to (251, 225, 1).

Contour

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ROI

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1st Dilation

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dilation is the same as the previous dilation.dilation is the same as the previous dilation.1-194-417-71-194-417-7

9th Dilation

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20th Dilation

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31st Dilation

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62nd Dilation

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ProcessProcess3.3.Break ROI into smaller patchesBreak ROI into smaller patches

Now MatLab has a map that tells it where the nerve clusters are in ourNow MatLab has a map that tells it where the nerve clusters are in our

image. Remember this is still in a marked image so we arent close toimage. Remember this is still in a marked image so we arent close to

solving the problem yet.solving the problem yet.

Convert the whole image to grayscale so we arent dealing with the 3Convert the whole image to grayscale so we arent dealing with the 3rdrddimension, RGB, anymore. This is done by taking the mean of thedimension, RGB, anymore. This is done by taking the mean of the

three RGB values.three RGB values.

In this project, we are using 5x5 patches, which means we areIn this project, we are using 5x5 patches, which means we are

looking at a box of 25 pixels.looking at a box of 25 pixels.

For this particular image, there are 2,911 5x5 different patches we can fitFor this particular image, there are 2,911 5x5 different patches we can fit

within the ROI.within the ROI.

We now have a (5x5x2,911) array of grayscale patches.We now have a (5x5x2,911) array of grayscale patches.

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ProcessProcess3.3.Break ROI into smaller patchesBreak ROI into smaller patches

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ProcessProcessHow do we define texture using statisticalHow do we define texture using statistical

measures?measures?

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ProcessProcess4.4. Find Statistical Measures of PatchesFind Statistical Measures of Patches

AA GLCMGLCM is a matrix that is calculated by how often a pixel withis a matrix that is calculated by how often a pixel with

grayscale valuegrayscale value iioccurs horizontally adjacent to a pixel withoccurs horizontally adjacent to a pixel with

valuevaluejj..

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ProcessProcess

4.4. Find Statistical Measures of PatchesFind Statistical Measures of Patches

AA Gray level CoGray level Co--Occurrence Matrix (GLCM)Occurrence Matrix (GLCM)

is a matrix that is calculated by how often a pixel with grayscaleis a matrix that is calculated by how often a pixel with grayscale

valuevalue iioccurs horizontally adjacent to a pixel with grayscaleoccurs horizontally adjacent to a pixel with grayscalevaluevaluejj..

The range of the image is scaled fromThe range of the image is scaled from

[0 255] to [1 8][0 255] to [1 8]

4747 5050 5454 5555 5454

5252 5454 5757 5858 5555

5858 5858 6060 6060 5757

5757 5555 5454 5353 5151

6060 5959 5757 5454 5353

11 33 55 55 44

44 55 66 77 55

88 88 77 66 55

77 66 66 66 55

88 88 77 66 55

Patch: Scaled Patch:

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ProcessProcess

4.4. Find Statistical Measures of PatchesFind Statistical Measures of PatchesExample: Find the GLCM of the patch.Example: Find the GLCM of the patch.

4747 5050 5454 5555 5454

5252 5454 5757 5858 5555

5858 5858 6060 6060 5757

5757 5555 5454 5353 5151

6060 5959 5757 5454 5353

11 33 55 55 44

44 55 66 77 55

88 88 77 66 55

77 66 66 66 55

88 88 77 66 55

Patch: Scaled Patch:

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ProcessProcess4.4. Find Statistical Measures of PatchesFind Statistical Measures of Patches

Properties of GLCMs to note:Properties of GLCMs to note:

1.1. The entries in the diagonal represent no change from one pixel toThe entries in the diagonal represent no change from one pixel to

the next.the next.2.2. As you move farther from the diagonal, it indicates greaterAs you move farther from the diagonal, it indicates greater

contrast.contrast.

3.3. The most contrast are those in the top right and bottom leftThe most contrast are those in the top right and bottom left

corners of the GLCM: entries (1,8) and (8,1).corners of the GLCM: entries (1,8) and (8,1).

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ProcessProcess


Gray CoGray Co--PropsProps is a tool used for evaluating a GLCM. It isis a tool used for evaluating a GLCM. It is

comprised of four properties: contrast, correlation, energy, andcomprised of four properties: contrast, correlation, energy, and

homogeneity.homogeneity.

The value of these four properties describes the GLCM, and in turn,The value of these four properties describes the GLCM, and in turn,

our ROI.our ROI.

To find these four properties, first turn all the elements of the GLCMTo find these four properties, first turn all the elements of the GLCM

into probabilities so that the sum of its elements is equal to 1.into probabilities so that the sum of its elements is equal to 1.

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ProcessProcess


i represents the GLCM row

j represents the GLCM column

p(i,j) represents the probability of the i,j entry

ji

jip,

2),(),(||2

,

jipjiji

Correlation:

Contrast: Energy:

ji ji

jip

, 1

),( ji ji

jipujjuii,

),())((WW

Homogeneity:

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ProcessProcess


a)a) Contrast:Contrast: returns a measure of the intensity contrast between areturns a measure of the intensity contrast between apixel and its neighbor over the whole patch.pixel and its neighbor over the whole patch.

),(|| 2

,

jipjiji

Range = [0 (size(GLCM,1)Range = [0 (size(GLCM,1)--1)^2]1)^2]

Contrastis 0 fora constantimage.Contrastis 0 fora constantimage.

A patchthatgoes from 1 to 8 and 8 to 1A patchthatgoes from 1 to 8 and 8 to 1repeatedly willhavethemaximum contrast.repeatedly willhavethemaximum contrast.

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ProcessProcess


b)b) Correlation:Correlation: Returns a measure of how correlated a pixel is toReturns a measure of how correlated a pixel is toits neighbor over the whole patch.its neighbor over the whole patch.

Range = [Range = [--1 1]1 1]

Correlationis 1 fora perfectly positivelyCorrelationis 1 fora perfectly positivelycorrelatedimage.correlatedimage.

CorrelationisCorrelationis --1 fora perfectlynegatively1 fora perfectlynegativelycorrelatedimage.correlatedimage.

CorrelationCorrelationisis NaNNaN fora constantimage.fora constantimage.

ji ji

jipujjuii

,

),())((WW

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ProcessProcess


c)c) Energy:Energy: Returns the sum of squared elements in the GLCM.Returns the sum of squared elements in the GLCM.

Range = [0 1]Range = [0 1]

Energyis 1 fora constantimage.Energyis 1 fora constantimage.

As energygets closertozero,itindicates anAs energygets closertozero,itindicates anincreaseof change fromone pixeltothenext.increaseof change fromone pixeltothenext.

ji jip,2

),(

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ProcessProcess


d)d) Homogeneity:Homogeneity: Returns a value that measure the closeness ofReturns a value that measure the closeness ofthe distribution of elements in the GLCM to the GLCM diagonal.the distribution of elements in the GLCM to the GLCM diagonal.

Range = [0 1]

Range = [0 1]

Homogeneityis 1 foradiagonal GLCM.Homogeneityis 1 foradiagonal GLCM.

Homogeneityis ameasureof smoothness.Homogeneityis ameasureof smoothness.

ji ji

jip

, 1

),(

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ProcessProcess

Example: Find the coExample: Find the co--propsprops

00 00 11 00 11 00 00 00

00 00 00 00 00 00 00 00

00 00 00 00 00 00 00 00

00 00 00 00 11 00 00 00

00 00 00 11 11 11 00 0000 00 00 00 33 22 11 00

00 00 00 00 11 33 00 00

00 00 00 00 00 00 22 22

GLCM:

12

3

4

5

6

7

8

1 2 3 4 5 6 7 8

00 00 .05.05 00 .05.05 00 00 00

00 00 00 00 00 00 00 00

00 00 00 00 00 00 00 00

00 00 00 00 .05.05 00 00 00

00 00 00 .05.05 .05.05 .05.05 00 0000 00 00 00 .15.15 .1.1 .05.05 00

00 00 00 00 .05.05 .15.15 00 00

00 00 00 00 00 00 .1.1 .1.1

1

2

3

4

56

7

8

1 2 3 4 5 6 7 8

GLCM: sum of elements equals 1

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ProcessProcess



),(|| 2

,

jipjiji

Range = [0 (size(GLCM,1)Range = [0 (size(GLCM,1)--1)^2]1)^2]

Contrastis 0 fora constantimage.Contrastis 0 fora constantimage.

A patchthatgoes from 1 to 8 and 8 to 1A patchthatgoes from 1 to 8 and 8 to 1repeatedly willhavethemaximum contrast.repeatedly willhavethemaximum contrast.

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ProcessProcess



),(|| 2

,

jipjiji

7/47/4oror1.751.75

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ProcessProcess



Range = [0 1]Range = [0 1]

Energyis 1 fora constantimage.Energyis 1 fora constantimage.

As energygets closertozero,itindicates anAs energygets closertozero,itindicates anincreaseof change fromone pixeltothenext.increaseof change fromone pixeltothenext.

ji jip,2

),(

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ProcessProcess



38/40038/400OrOr.095.095

ji jip,2

),(

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ProcessProcess



Range = [0 1]

Range = [0 1]

Homogeneityis 1 foradiagonal GLCM.Homogeneityis 1 foradiagonal GLCM.

Homogeneityis ameasureof smoothness.Homogeneityis ameasureof smoothness.

ji ji

jip

, 1

),(

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ProcessProcess



143/240Or.5958

ji ji

jip

, 1

),(

Documents

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