BG3801 L3 Medical Image Processing 14-15

8/11/2019 BG3801 L3 Medical Image Processing 14-15

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School of Chemical and Biomedical Engineering

Division of Bioengineering

BG3801: Bioengineering Laboratory

Year 3, Semester 1

Medical Image Processing

Location: [N1.3-B4-02]

Name: _______________________________________

Matric Number: _______________________________________

Group: _______________________________________

Date of experiment: _______________________________________

GRADE: _____________


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Medical Image Processing

1.

INTRODUCTION

Image processing is an important field with applications in a number of areas

(e.g., neuroimaging, surgical planning, etc). Medical image processing generallyinvolves the study of feature extraction, segmentation, and classification

techniques. The aim in image processing applications is to extract important

features from the image data, from which a description, interpretation, orunderstanding of the disease can be provided by the machine.

2. OBJECTIVES

In this practical you will

1. learn to use Matlab to display and manipulate medical images

2. filter medical images using high pass filter for edge detection

3. filter medical images using low pass filter for noise reduction

4. learn to perform windowing/leveling of medical images using Matlab.(optional)

3.

BACKGROUND

3.1 Digital Images

Digital images are made of a matrix of pixels. For grayscale images each pixel

stores a value between 0 and 255 (in the case of 8 bit images) where 0 representsblack, 255 represents white and other numbers represent a varying shade of gray

from black to white. In the case of grayscale medical images, each pixel is 16 bits

(each pixel stores a value between 0 and 216

). This provides the medical imageswith a larger range of shade of gray. The resolution of the image is defined by the

number of pixels making up the image. Hence, the larger the number of pixels

making up the images, the higher the resolution of the images. Consequently, theimage will have more details.

3.2 Spatial Filtering

Filters are widely used for pre-processing of digital images to remove differentkinds of noise, detect edges and boundaries, detect particular shapes in the image

etc. Filtering can be performed both in spatial and frequency domain. In this


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practical, we will only focus on spatial filtering. Spatial filtering involves filtering

operations that are performed directly on the pixels of an image. The procedures

of spatial filtering are illustrated in Figure 1. The process involves moving thefilter mask from point to point in an image. A mask is defined as a small (e.g., 3 x

3) 2-D array in which the values of the mask coefficients determine the nature of

the process, such as image smoothing, edge detection, etc. For linear spatialfiltering, the response at each point (x, y) is given by a sum of products of thefilter coefficients and the corresponding image pixels in the area spanned by the

filter mask.

Figure 1. The mechanics of spatial filtering. The magnified drawing shows a 3 x 3

mask and the image section directly under it [1].


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In general, linear filtering of an image f of size M x N with a filter mask of size m

x n is given by the expression:

a

as

b

bt

filt tysxftswyx ),(),(, (3.1)

where

2/)1( ma ;2/)1( nb ;

1,....,2,1,0 M ;1,....,2,1,0 N

Referring to the equation, it is important to note that the mask processes all pixelsin the image. The process of linear spatial filtering is similar to a frequency

domain concept called convolution. For this reason, linear spatial filtering often is

referred to as convolving a mask with an image. Similarly, filter masks aresometimes called convolution masks.

High Pass filters (Edge Detection)

Edge detection is used for detecting meaningful discontinuities in an image in

gray level. An edge can be defined as a set of connected pixels that lie on the

boundary between two regions. Edge detection filters (also refers to as high-pass

filters) uses spatial first/second order derivatives of intensity function to enhanceintensity variation across the edge.

(i) First-order derivatives filters

First-order derivatives in a digital image in gray level can be calculated using the

gradient. The calculation of the derivatives of an image is based on variousapproximations of the 2-D gradient. Edges are located at maxima of absolute

value of first order derivatives. The gradient of an image f(x, y) at location (x, y)

is defined as the vector

y

fx

f

G

Gf

y

x

(3.2)

The magnitude and direction of the gradient vector are obtained as follows:


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22)( yx GGfmag (3.3)

x

y

G

Gyx

1tan),(

(3.4)

The gradient of an image can be calculated by obtaining the partial derivativesand at every pixel location. For example, consider a 3 x 3 area shown in Fig

below representing the gray levels in a neighborhood of an image.

1z

2z

3z

4z

5z

6z

7z

8z

9z

Figure 2. A 3 x 3 area that represent the gray levels in a neighborhood of an

image.

The derivate can be approximated using masks of size 3 x 3 by the followingequations:

)()( 321987 zzzzzzGx (3.5)

)()( 741963 zzzzzzGy (3.6)

Referring to the equations, the difference between the first and third rows of the 3

x 3 image region approximates the derivative in the x-direction (Gx), and thedifference between the third and first columns approximates the derivative in they-direction (Gy). These two equations can be implemented using the mask shown

below. These marks are calledPrewitt operators.

-1 0 1

-1 0 1

-1 0 1

yG

-1 -1 -10 0 0

1 1 1

xG Figure 3. The Prewitt operators


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(ii) Second-order derivatives filters - The Laplacian

Edge detection can also be performed using second-order derivatives. TheLaplacian is an example of second-order derivative operator. The Laplacian

operator highlights gray-level discontinuities in an image and deemphasizes

regions with slowly varying gray levels.

The digital implementation of the two-dimensional Laplacian is as follow:

),(4

)]1,()1,(),1(),1([2

yxf

yxfyxfyxfyxff

This equation can be implemented using the mask shown in Figure 4 below. The

methods of implementation are similar to the linear smoothing filters. The main

difference is the values of the coefficients of the filters.

111

181

111

Figure 4. An example of Laplacian operators

Second order derivatives have a stronger response to fine detail, such as thin linesand isolated points whereas first order derivatives generally have a stronger

response to a gray-level step.

Low Pass filters (Smoothing Spatial filters)

Low pass filters (smoothing filters) are used to reduce the noise in digital images.

The resulting processed image after using a smoothing, linear spatial filter is theaverage of the pixels contained in the neighborhood of the filter mask. These

filters are also called averaging filters/lowpass filters.

The concept behind smoothing filters is to replace the value of every pixels in animage by the average of the gray levels in the neighborhood defined by the filter

mask.

111

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Figure 5. A 3 x 3 smoothing linear filter

Figure 5 above shows a 3 x 3 smoothing filters. The use of the filter produces the

standard average of the pixels under the mask. For example, consider a 3 x 3 areaas shown in Figure 2, using the 3 x 3 smoothing filter at location of Z 5, the value


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of Z5will be replaced by an average of the values of Z1 to Z9. This process is

carried out at every pixel location. The results of smoothing are affected by the

filter size. We will study this effect in this practical (Exercise 2).

2. LABORATORY

Bio-Computing Lab. (N1.3 B2-25)

3. EQUIPMENT

Personal Computers (Window NT 4.0) and MATLAB software (Ver 6.1).

4. IMAGE FLES

All the image files you will use in this lab can be found on the lab PCs in Network

directory\\scbefs1\BG3701-4.

5. EXERCISE 1In this section of the practical, we will be using Matlab routines for reading

DICOM 3.0 files from disk. DICOM 3.0 is an international standard for the

encoding and communication of medical image data. Images are stored as amatrix in Matlab. Hence matrix operations can be performed on images.

5.1Displaying DICOM 3.0 Images

1. First create a new folder, Medical Image Processing in your network

directory. This folder will be your working directory where you will save all the

programs written in this practical. Now specify the Matlab working directory asyour new folder, as illustrated in Figure 6. Remember to save all the files in this

folder.

Figure 6. Defining working directory in Matlab.

2. Copy the images in the Network directory BG3701-4 into your working

directory.

3. Use the following command to obtain the header information from the file

>> info = dicominfo(MR_Image);
http://scbefs1/BG3701-4http://scbefs1/BG3701-4http://scbefs1/BG3701-4http://scbefs1/BG3701-4


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Note: The file name is case-sensitive.

See if you can interpret some of the information in this structure.

To view the values in the variable info, you can use the following command

>> info

Or double click on the variable info in the workspace.

4. Use the following command to read the image.

>> I = dicomread (MR_Image);

5. In order to display the image, use the following command:

>> imshow (I, []);

Because the image data in this DICOM file is signed 16-bit data, you must use the

autoscaling syntax [ ] in Matlab to view the image.

You should see the image as shown below.

Figure 7. A figure window showing the MR image of the Knee.

5.2 Displaying multiple images

Because MRI and CT are tomographic imaging techniques, the image data

generally comprises a series of images covering a volume of interest, e.g. thebrain, knee, etc. Hence, the images are often display as sequence of images side


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by side. This is the standard way of creating a hard-copy of multi-image data.

This can be performed using the montagefunction in Matlab.

1. Use the following command to load a MR image volume into the workspace

>> load mri;>> figure, montage (D, map);

The second command (montage) above creates what is known as an image

montage.

6. EXERCISE 2

In this exercise, we will study the effects of different kind of high-pass and low-

pass filters on medical images. We will analyze the positive and negative effects

of using filters on medical images.

6.1 High Pass filter (Edge Detection)

In this section of the practical, you will filter both a digital photo and medical

image using the following filter masks.

a)

101

101

101

3

1 (b)

111

000

111

3

1 (c)

111

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111

(i) Digital PhotoFirst we will filter a digital photo in JPEG format.

1. Use the following command to load and view the image.

>> I_photo = imread('cameraman.tif');>> figure, imshow(I_photo);

2. Use the following command to define the filter (a) in Matlab

>> A1 = 1/3 * [-1 0 1-1 0 1

-1 0 1];

Note: Hit enter after keying each row. When it reaches the last row, Matlab willstore the value of the filter into variable A1.

3. Use the following command to filter the image I_photo:


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>> I_photo_filtered = imfilter(I_photo, A1);

Matlab provides imfilterfunction to filter images.

4. Use the following command to display the images (before and after filtering)

>> figure, subplot(1,2,1); imshow(I_photo);

>> subplot(1,2,2); imshow(I_photo_filtered);

What do you think is being highlighted in the image by filter A?

Now using similar commands in steps 2 - 4, filter the same digital photo using

filter B and C?

Question 1.

What do you think is being highlighted in the image by these filters?

(ii) Medical Image

4. Use the following command to load the MR image into Matlab workspace.

>> I = dicomread (MR_Image);

Following the same steps described in the above section for digital photo, filter

the medical image using the 3 filters.

Question 2.

What did you observe? Comment on the results.

6.2 Low Pass Filters (Noise Removal)

Images contaminated by speckle noise can be processed using a smoothing spatial

filter (also called low pass filter). Filter the image with the following averagingfilters and study how the size of the filter affects the image.

1. Load image using the following command

>> I_noise = dicomread(MR_noise);

The MR image has been contaminated by speckle noise.


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d)

111

111

111

9

1 e)

11111

11111

11111

11111

11111

25

1 f)

1111111

1111111

1111111

1111111

1111111

1111111

1111111

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1

2. Use the following command to create filter (d)

>> A2 = 1/9 * ones(3, 3);

ones(m, n) function returns a m-by-n matrix of ones, where m = columns and n =rows.

3. Using the imfilterfunction, filter the medical image using averaging filter (d).

4. Repeat step 2 and 3 using averaging filters (e) and (f).

Question 3.

Comments on your results. How does the size of the filter affect the image, e.g the

edges?

6.3 Windowing and leveling (Optional)

Because medical image data contains 16 bits data, it is a common practice to usewindowing and leveling to change the contrast and brightness of the images inorder to highlight the anatomy structure of interest.

>> imshow(MR_Image)>> imcontrast(gca)

The Matlab imcontrast function allows you to interactively change the contrastand brightness of the image (windowing/leveling).

Clicking and dragging the mouse within the image interactively changesthe image's window values.

Dragging the mouse horizontally from left to right changes the windowwidth (i.e., contrast).

Dragging the mouse vertically up and down changes the window center(i.e., brightness).


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7. Exercise 3 (Using the images acquired at PWG)

Images that you have acquired at PWG are available at \\BIOE-

B402S1035\users\latifah\Documents\BG3701\AY13-14. There are two folders in the shared

drive One for CT and one for MRI. Under each folder, each set of images arestored in subfolder named after the name of one of the students in the subgroup.

The aim of this exercise is to view, process and analyze the CT and MRI images

of the bone sample that you have imaged using microCT and MRI.

Throughout this exercise, we will be using a software ImageJ

(http://rsbweb.nih.gov/ij/)to view the images. ImageJ is a freely available image

processing tool developed by National Institutes of Health. Here is a briefintroduction to the software. More details can be found at

http://rsbweb.nih.gov/ij/docs/pdfs/ImageJ.pdf

a) To start the program, double click on ij.exe in the folder. The following screenshould pop up on your screen. This is the main user interface.

Figure 7.1 A screen of the interface for ImageJ

b) Open the images

i. To import the images that you like to view, go to File->Import-> ImageSequence. Select the first image in the folder and click open. See Figure

7.2a

ii. A diagloue box will then pop-up, click ok. This diagloe box shows thenumber of images that you will be reading into ImageJ. See Figure 7.2b
http://bioe-b402s1035/users/latifah/Documents/BG3701http://bioe-b402s1035/users/latifah/Documents/BG3701http://rsbweb.nih.gov/ij/http://rsbweb.nih.gov/ij/http://rsbweb.nih.gov/ij/http://rsbweb.nih.gov/ij/docs/pdfs/ImageJ.pdfhttp://rsbweb.nih.gov/ij/docs/pdfs/ImageJ.pdfhttp://rsbweb.nih.gov/ij/docs/pdfs/ImageJ.pdfhttp://rsbweb.nih.gov/ij/http://bioe-b402s1035/users/latifah/Documents/BG3701http://bioe-b402s1035/users/latifah/Documents/BG3701


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(a) (b)Figure 7.2

c) After step b, you should see the images similar to Figure 7.3. To scroll theimages, use the scroll bar at the bottom of the viewing box.

Figure 7.3


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d) Changing the contrast and brightness of the images

It is common for us to change the contrast and brightness of the images. InImageJ, the tool to adjust contrast and brightness is available at Image->Adjust-

>Brightness/Contrast. You may also use Window/Level. A separate dialogue box

will appear. See Fig. 7.4

Figure 7.4

7.1 microCTThree different views of the sample were acquired. These views are the transverse

plane, coronal plane and sagittal plane. Figure 8 below illustrates the orientation

of the three planes.

Figure 8 showing transverse (axial), sagittal, and coronal planes.(http://en.wikipedia.org/wiki/Sagittal_plane)
http://en.wikipedia.org/wiki/Sagittal_planehttp://en.wikipedia.org/wiki/Sagittal_planehttp://en.wikipedia.org/wiki/Sagittal_planehttp://en.wikipedia.org/wiki/Sagittal_plane


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Questions 4

i) In your logsheet, copy and paste one representative image from each planeand label clearly. Note down the slice thickness and image resolution of

the images i.e. voxel size. You can obtain this information in the image

header. To view header information using ImageJ, go to Image->ShowInfo

ii) Choose one of the best CT images from the set acquired in the sagittalplane and label the bone, muscle, screws by drawing arrows to point at the

structures. Discuss the quality of the CT images, e.g. is the contrast good?Are there artifacts?

iii) Choose one of the CT images in the sagittal plane that best display thescrew, and then measure the distance between the two screws using

ImageJ. Note down this measurement. To perform measurement usingImageJ, refer to the figure below. To retrieve the measurement, go to

Analyze->Measure. A box will appear. Under the length column will be

the length of the line you have drawn. Discuss the potential errors thatcould occur during the measurement.


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iv) Image processing: using the same image that you chose in (ii), apply theHigh Pass filter (Edge Detection) using the mask (a) in 6.1. Commenton your answer.

(a)

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v) Image processing: using the same image that you chose in (ii), apply theLow Pass filter (Smoothing) using the mask (d) in 6.2. Comment onyour answer.

(d)

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7.2 MRI

Three different views of the sample were acquired. These views are the transverseplane, coronal plane and sagittal plane.

Questions 5

i) In your logsheet, copy and paste one MR image from each plane and labelclearly. By reading the Dicom header of the images (refer to previous


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section 5.1 Displaying DICOM 3.0 Images), note down the TE, TR and

flip angle for the MR acquisition in the sagittal plane.

ii) Choose one of the best images from the set acquired in the sagittal planeand label the bone, muscle, screws by drawing arrows to point at the

structures. Discuss the quality of the MR images, e.g. is the contrast good?

iii) Image processing: using the same MR image that you chose in (ii), applythe High Pass filter (Edge Detection) using the mask (a) in 6.1.

Comment on your answer.

(a)

101

101

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1

iv) Image processing: using the same image that you chose in (ii), apply theLow Pass filter (Smoothing) using the mask (d) in 6.2. Comment on

your answer.

(d)

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8. REFERENCES

1. Gonzalez, R. C. and Woods, R. E., Digital Image Processing, Addison-Wesley, 1992.

2. K. R. Castleman, Digital Image Processing, Prentice-Hall, 1996

3. Getting Started with MATLAB. The MathWork, Inc., Natick, MA, 1984-1997.

4. Using MATLAB. The MathWork, Inc., Natick, MA, 1984-1998.

5. Image Processing Toolbox User's Guide, The MathWork, Inc., Natick,MA, 1993-1997.


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9. APPENDIX

Guidelines and Hints for Image Processing in MATLAB.

Medical Image Input and Manipulation

dicomreadis used to load medical image data in DICOM standard. The basic syntax is I= dicomread(filename). The image data from the file will be stored in matrix I.

dicominfois used to load the header information of medical image data in DICOMstandard. The basic syntax is Info = dicominfo(filename). The header information from

the image data will be stored in matrix Info.

imshowdisplays the data of an image file in a MATLAB figure window.

Figure 9. Details about the anatomy shown in the image. The image is showing

the knee acquired in the sagittal plane.