chapter iii preprocessing &...

CAD SYSTEM FOR AUTOMATIC DETECTION OF BRAIN TUMOR THROUGH MRI

PREPROCESSING &ENHANCEMENT

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CHAPTER III

PREPROCESSING &

ENHANCEMENT



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CHAPTER 3

PREPROCESSING AND ENHANCEMENT

3.1 PREFACE

Image processing and enhancement stage is the simplest categories of medical

image processing. This stage is used for reducing image noise, highlighting edges, or

displaying digital images. Some more techniques can employ medical image processing of

coherent echo signals prior to image generation. The enhancement stage includes resolution

enhancement and contrast enhancement. These are used to suppress noise and imaging of

spectral parameters. After this stage the medical image is converted into standard image

without noise, film artifacts and labels.

Image enhancement methods inquire about how to improve the visual appearance of

images from Magnetic Resonance Image (MRI), Computer Tomography (CT) scan; Positron

Emission Tomography (PET) and the contrast enhancing brain volumes are linearly aligned.

The enhancement activities are removal of film artifacts and labels, filtering the images.

This part is use to enhances the smoothness towards piecewise-homogeneous region and

reduces the edge-blurring effect. Conventional Enhancement techniques such as Low pass

filter, Median filter, Gabor Filter, Gaussian Filter and Prewitt edge-finding

filter[29,43,72,87].



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3.2 RELATED WORK

The Preprocessing aspects are surveyed and analyzed in this section. The

Preprocessing techniques such as Content Based model, Fiber tracking Method, Wavelets ,

Wavelet Packets, and Fourier transform technique [Jeyaram et al, Peter et al, Azadeh et al,

Karen et al,] [60,61,99,100,10,11,66 ].

Olivier et al. designed a new Standard Imaging Protocol for brain tumor

radiotherapy. MRI has been acquired in the standard follow up after surgical resection [94].

Dana et al presented statistical parametric mapping implementation and pipeline approach

for registration and resampling stages. The pipeline consists of noise reduction and inter-

slice intensity variation correction [28,68]. Elizabeth et al explained Pixel Histograms and

Morphological process for acquiring brain image from MRI. It was more robust to noise [12,

41, 81, 82, 88, 91]. Leung et al described Boundary Detection Algorithm, Generalized Fuzzy

Operator (GFO), Contour Deformable Model, and Region base technique for image

processing applied in radiology for 3D reconstruction [76]. Patrick et al developed a new

Boundary Model and Non linear matching scheme to estimate the location of the boundary

points using intensity data with standardized data [98,120]. Azadeh et al designed a method

on Wavelets & Wavelet Packets for noise reduction and correcting baseline [10, 11].

Paulo proposed a method of Fiber tracking to process MR-DT1 datasets. Karen et al

represented a Fourier transform technique for MRI preprocessing [66]. Lorenzen et al

designed a Geometric prior image registration [99,100]. Xin et al. presented Unseeded

Region Growing (URG) Algorithm use to convert the MRI image into standard Format



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[132]. Zu et al, analyzed a new method on Sub-second imaging technique and histogram

based technique Separate brain image from head image removal of residual fragments

[108,142]. Xiao et al described an automated method on Statistical Structure Analysis

method for analyzing the images from MRI [129]. Brian et al, designed Principal

Component for to minimize the artifacts present in the PET data set [17].Mark et al,

described a new method on Statistical Parametric Mapping for spatial registration and

resampling stages used the T1 single subject template and used existing implementation for

the intensity normalization stages [28,84].Shishir et al, presented a histogram method for

improving the quality of MR brain images [111].Toshiharu et al, said a Independent

Component Analysis (ICA) method for separate the components in MR images into

independent components (IC’s) [122].

Normalization Method [131,33,6,74,119][Xiangyang Wang ,Dimirits ,Ladan Amini ,

Thomas P Ryan] are employable for this work. Dimitris et al. presented a new method on

Gabor Filter applied to remove the tagging lines and enhance the tag-patterned regions in the

image. Tag Patterns in the blood are flushed out very soon [33]. Karnan et al. designed a

new CAD system for Image enhancement using median filter [83,118]. Tsai et al. studied

low pass filtered to take care of local noisy fluctuations, the bone and soft tissue outlines are

eliminated [123].

Boada et al represented that Triple Quantum Filter can be used to minimize the

effects of extra cellular Fluids on the Measurement of the intracellular sodium concentration

[16]. Marcel Prastawa presented Anisotropic Diffusion filter the registered images [12, 51].



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Ladan et al. studied Edge Finding filter for Reducing noise and prewitt filter for improving

the Image quality [74] Aria et al. described Gadolinium Enhanced tumor borders when

relation between Tumor cell extent and contrast-enhanced region is Unclear from MRI [9].

Amini et al. presented Prewitt edge-finding filter to enhance the image Edges robustly [6,

74]. Zhe et al. studied a new method for automatic detection of PET lesions using

Morphological Operations for removing Backgrounds from brain images [137]. Xiao et al

Designed Gabor Filter for filtering noise from MRI Brain tumor image and partition the

frequency space with equal angle of 30 degrees in angular direction [129]. Corina et al.

designed Gaussian filter is applied to the image to enhance its boundaries and Make the

image gradients stronger [27].Shishir et al described a nonlinear filter for removing noise

from Given MR brain images [11].

3.3 IMAGE ACQUISITION

Detection of brain tumor requires high-resolution brain MRI. Most Medical Imaging

Studies and detection conducted using MRI, Positron Emission Tomography (PET) and

Computer Tomography (CT) Scan. Now a day’s MRI systems are very important in medical

image analysis. MRI has a multidimensional nature of data provided from different

sequential pulses.

A MRI (Magnetic Resonance Imaging) scan is a radiology technique that uses

magnetism, radio waves, and a computer to produce images of body structures. The MRI

scanner is a tube surrounded by a giant circular magnet. The patient is placed on a moveable

bed that is inserted into the magnet. The magnet creates a strong magnetic field that aligns



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the protons of hydrogen atoms, which are then exposed to a beam of radio waves. This spins

the various protons of the body, and they produce a faint signal that is detected by the

receiver portion of the MRI scanner.

The receiver information is processed by a computer, and an image is produced. The

image and resolution produced by MRI is quite detailed and can detect tiny changes of

structures within the body. For some procedures, contrast agents, such as gadolinium are

used to increase the accuracy of the images. MR images result from the excitation of

hydrogen protons by Radio Frequency (RF) pulses.

The MRI machine generates very brief RF Pulses; these RF pulses excite hydrogen,

and elevate them to a higher energy state. As the protons return to a lower energy state, they

relies electromagnetic energy. This energy is picked up and amplified by the magnet’s

antennae and turned into visual display images. A MRI scan can be used as an extremely

accurate method of disease detection throughout the body. In the head, trauma to the brain

can be seen as bleeding or swelling. Other abnormalities often found include brain

aneurysms, stroke, tumors of the brain, as well as tumors or inflammation of the spine.Neuro

surgeons use a MRI scan not only in defining brain anatomy but in evaluating the integrity

of the spinal cord after trauma. It is also used when considering problems associated with

the vertebrae or inter vertebral discs of the spine.



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A MRI scan can evaluate the structure of the heart and aorta, where it can detect

aneurysms or tears. MRI scanners can produce 1500 images per second. Intra operative

Magnetic resonance imaging can acquire high contrast images of Soft tissue anatomy. MRI

is the modality of choice for evaluating brain morphology because it provides superior soft-

tissue contrast with flexible data acquisition protocols that highlight several different

properties of the tissue.

It provides valuable information on glands and organs within the abdomen, and

accurate information about the structure of the joints, soft tissues, and bones of the body.

Often, surgery can be deferred or more accurately directed after knowing the results of a

MRI scan.

MRI scanning uses magnetism, radio waves, and a computer to produce images of

body structures. MRI scanning is painless and does not involve x-ray radiation. Patients with

heart pacemakers, metal implants, or metal chips or clips in or around the eyes cannot be

scanned with MRI because of the effect of the magnet. Claustrophobic sensation can occur

with MRI scanning.

Images of a patient obtained by CT, MRI and SPECT, PET scanning are displayed as

an array of pixels (a two dimensional unit based on the matrix size and the field of view) and

stored in memory.In Matlab, there are several formats of image encoding, in this research

the MR image default size is 256 x 256.

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Fig

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3.1

Im

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The above appearing MR Brain image consists following attributes

Table 3.1 Image Attributes

Attribute Name Description

Filename 'brain.jpg'

File Mod Date '21-Apr-2010 10:48:38'

File Size 21057

Format 'jpg'

Width 256

Height 256

Bit Depth 8

ColourType 'grayscale'

Number of Samples 1

Coding Method 'Huffman'

Coding Process 'Sequential'

Comment {}



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3.4. GRAYSCALE OR INTENSITY MRI IMAGES

A grayscale image can be specified by giving a large matrix whose entries are

numbers between 0 and 255, with 0 to black, and 255 to white.

.

Figure 3.2 MRI image

To Access the real medical images like MRI, PET (Positron Emission Tomography)

or CT (Computer Tomography) scan and to take up a research is a very complex because of

privacy issues and heavy technical hurdles. The purpose of this research is to compare

Automatic Brain Tumor Detection methods through MRI Brain Images. MRI Images are

transformed to a Linux Network through LAN (Local Area Network) Kovai Medical Center

Hospital (KMCH), Coimbatore, India. All images had 1 mm slice thickness with 1×1 mm in

plane resolution.

The development of intra-operative imaging systems has contributed to the

improvement of the course of intracranial neurosurgical procedures. Among these systems,

the 0.5T intra-operative Magnetic Resonance Scanner of the Kovai Medical Center and



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Hospital (KMCH ) offer the possibility to acquire 256*256*58(0.86mm, 0.86mm, 2.5 mm)

T1 weighted images with the fast spin echo protocol (TR=400, TE=16 ms, FOV=220*220

mm) in 3 minutes and 40 seconds. The quality of every 256*256 slice acquired intra-

operatively is fairly similar to images acquired with a 1.5T Conventional Scanner, but the

major drawback of intra-operative image is that slice remains thick (2.5 mm).

3.4.1 LOAD IMAGE TO MATLAB

Read image file from the mammogram folder using matlab default function

Uigetfile, this function displays a dialog box used to retrieve one or more files. The dialog

box lists the files and directories in the current directory. Uigetfile returns the name and path

of the file selected in the dialog box. After the user clicks the Done button, FileName

contains the name of the file selected and PathName contains the name of the path selected.

If the user clicks the Cancel button or closes the dialog window, FileName and PathName

are set to 0, successful return occurs only if all the selected files exist. If the user selects a

file that does not exist, an error message is displayed and control returns to the dialog box.

Example

[filename, pathname] =

uigetfile({'*.tif';'*.jpeg';'*.png';'*.jpg';'*.tiff';'*.*'},'Open File');

3.4.2 CONVERT IMAGE TO DIGITAL MATRIX

Loaded image convert digital matrix with use of matlab library function imread.

This function convert image to digital matrix. Imread (filename,fmt) reads a grayscale or

colour image from the file specified by the string filename, where the string fmt specifies the

format of the file. If the file is not in the current directory or in a directory in the MATLAB



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path, specify the full pathname of the location on your system. For a list of all the possible

values for fmt. If imread cannot find a file named filename, it looks for a file named

filename.fmt. imread returns the image data in the array A. If the file contains a grayscale

image, A is a two-dimensional (M-by-N) array. If the file contains a colour image, A is a

three-dimensional (M-by-N-by-3) array. The class of the returned array depends on the data

type used by the file format. For most file formats, the colour image data returned uses the

RGB colour space. For TIFF files, however, imread can return colour data that uses the

RGB, CIELAB, ICCLAB, or CMYK colour spaces. If the colour image uses the CMYK

colour space, A is an M-by-N-by-4 array.

Example:

[filename,pathname]= uigetfile({'*.pgm';'*.tif';'*.jpeg';'*.png';'*.jpg';'*.tiff';'*.*'},'Open

File');

f=filename;

j=imread(f);

Image converted to digital matrix , image size reduced for displaying the image in the

system using imresize function , imresize(A,m,method) returns an image that is m times the

size of A using the interpolation method specified by method. Method is a string that can

have one of these values. The default value is enclosed in braces ({}).Value Description

{'nearest'} Nearest-neighbour interpolation 'bilinear' Bilinear interpolation 'bicubic' Bicubic

interpolation

Example

I = imresize (i,[255 255],'nearest');



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3.4.3 DISPLAYING IMAGE

Resized image displayed in matlab system to shows the image in imtool box by use

of imshow function. imshow (I,[low high]) displays the grayscale image I, specifying the

display range for I in [low high]. The value low (and any value less than low) displays as

black; the value high (and any value greater than high) displays as white. Values in between

are displayed as intermediate shades of gray, using the default number of gray levels. If you

use an empty matrix ([]) for [low high], imshow uses [min(I(:)) max(I(:))]; that is, the

minimum value in I is displayed as black, and the maximum value is displayed as white.

imshow is the toolbox's fundamental image display function, optimizing figure, axes, and

image object property settings for image display. imtool provides all the image display

capabilities of imshow but also provides access to several other tools for navigating and

exploring images, such as the Pixel Region tool, Image Information tool, and the Adjust

Contrast tool. imtool presents an integrated environment for displaying images and

performing some common image processing tasks.

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M/N

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Fig

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3.3

In

ten

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igit

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Matr

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of

the

MR

I im

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3.5 PRE-PROCESSING

Preprocessing indicates that the same tissue type may have a different scale of signal

intensities for different images. Preprocessing functions involve those operations that are

normally required prior to the main data analysis and extraction of information and are generally

grouped as radiometric or geometric corrections. Radiometric corrections include correcting the

data for sensor irregularities and unwanted sensor or atmospheric noise, removal of non-brain

voxels and converting the data so they accurately represent the reflected or emitted radiation to

find out a transformation between two images precisely.The preprocessed images will have some

noise which should be removed for the further processing of the image. Image noise is most

apparent in image regions with low signal level such as shadow regions or under exposed

images. There are so many types of noise like salt – and – pepper noise, film grains etc., All

these noise are removed by using algorithms. Among the several filters, median filter is used.

3.5.1 REMOVAL OF UNWANTED PARTS FROM THE BRAIN MR IMAGE

In preprocessing module image acquired will be processed for correct output. Medical

images surely will have some Film Artifacts like labels, marks and unwanted or critical parts

which are detected and removed for better result. Pre-processing was done by using some

algorithm. For all images the pre-processing should be done so that the result can be obtained in

the better way. To find out the transformation between two images precisely they should be

preprocessed to improve their quality and accuracy of result. If these images are too noisy or

blurred, they should be filtered and sharpened.

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(a)

Fil

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MR

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Aft

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3.5

(d

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3.5.2 FINDING EDGE POSITION FOR REMOVING LABELS

Consider breast tissue have an intensity value 10, set maximum intensity value for

greater than 10 intensity value positions. All tissue and labels set 255 intensity. Next use of

tracking algorithm remove the labels and unwanted film artifacts. For removing the

unwanted portions of the image, Tracking Algorithm is used.

for x=1:m

flag=0;

for y=n:-1:1

if( (flag==0) && (i2(x,y)<=35))

flag=1;

else

i3(x,y)=i1(x,y);

end

if(flag==1)

i3(x,y)=0;

end

end

end

Figure 3.6 Tracking Algorithm



3.6 IMAGE SMOOTHING

The aim of image smoothing is to diminish the effects of noise, spurious pixel

values, missing pixel values etc. There are many different techniques for image smoothing.

The neighbourhood averaging and edge-preserving smoothing are used in image smoothing.

NEIGHBOURHOOD AVERAGING

Each point in the smoothed image, F(x,y) is obtained from the average pixel value in

a neighbourhood of (x,y) in the input image. For example, if 3x3 neighbourhood around

each pixel use the mask. Each pixel value is multiplied by 1/9, summed, and then the result

placed in the output image. This mask is successively moved across the image until every

pixel has been covered. That is, the image is convolved with this smoothing mask also

known as a spatial filter or kernel. However, one usually expects the value of a pixel to be

more closely related to the values of pixels close to it than to those further away.

1/9 1/9 1/9

1/9 1/9 1/9

1/9 1/9 1/9

Fig 3.7 Neighbourhood Averaging



Some common weighting functions include the rectangular weighting function above

which just takes the average over the window, a triangular weighting function, or a

Gaussian. In practice one doesn't notice much difference between different weighting

functions, although Gaussian smoothing is the most commonly used. Gaussian smoothing

has the attribute that the frequency components of the image are modified in a smooth

manner. Smoothing reduces or attenuates the higher frequencies in the image.

EDGE PRESERVING SMOOTHING

Neighbourhood averaging or Gaussian smoothing will tend to blur edges because the

high frequencies in the image are attenuated. An alternative approach is to use median

filtering. Here the grey level is the median of the pixel values in the neighbourhood pixel.

The median m of a set of values is such that half the values in the set are less than ‘m’ and

half are greater. For example suppose the pixel values in a 3x3 neighbourhood are (10, 20,

20, 15, 20, 20, 20, 25, 100). If the values are sorted (10, 15, 20, 20, |20|, 20, 20, 25, 100) ,

the median is 20. The outcome of median filtering is that pixels with outlying values are

forced to become more like their neighbour, but at the same time edges are preserved.

IMAGE SHARPENING

The main aim in image sharpening is to highlight fine detail in the image, or

to enhance detail that has been blurred (perhaps due to noise or other effects, such as

motion). With image sharpening, the high-frequency components are enhanced, this implies

a spatial filter shape that has a high positive component at the centre as shown in figure 3.8.

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Fre

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A simple spatial filter that achieves image sharpening is given by

-1/9 -1/9 -1/9

-1/9 8/9 -1/9

-1/9 -1/9 -1/9

Figure 3.9 spatial filter using image sharpening

Since the sum of all the weights is zero the resulting signal will have a zero DC value

i.e. the average signal value or the coefficient of the zero frequency term in the Fourier

expansion. For display purposes, the value of an offset to keep the result in the 0….255

range.

HIGH BOOST FILTERING

High pass filtering is achieved from subtracting a low pass image from the original

image, i.e. High pass = Original - Low pass.

However, in many cases where a high pass image is required, and retain some of the

low frequency components to aid in the interpretation of the image. Thus, if multiplying the

original image by an amplification factor before subtracting the low pass image, the result is

high boost or high frequency emphasis filter. Thus,



High boost = A. Original – Low pass

= (A- 1).(Original) + Original – Low pass

= (A-1).Original + High pass

Now, if A = 1 it is a simple high pass filter. When A > 1 part of the original image is

retained in the output.

A simple filter for high boost filtering is given by

-1/9 -1/9 -1/9

-1/9 /9 -1/9

-1/9 -1/9 -1/9

Figure 3.10 simple high pass filter

Where ω = 9A-1



3.7 ENHANCEMENT

The proposed system describes the information of enhancement using four types of

filters such as

1. Median filter

2. Weighted Median filter

3. Adaptive filter

4. Spatial filter

for removing high frequency components such as impulsive noise, salt and pepper noise and

high frequency components. In the Enhancement stage the filters are designed to enhance the

appearance of images, primarily by sharpening Edges, corners, and line detail. Several of the

new enhancement filters also incorporate a noise-reduction component.

Median filtering is a nonlinear operation often used in image processing to reduce

"salt and pepper" noise. Median filtering is more effective than convolution when the goal is

to simultaneously reduce noise and preserve edges.

If the input image A is of an integer class, all the output values are returned as

integers. If the number of pixels in the neighbourhood (i.e., m*n) is even, some of the

median values might not be integers. In these cases, the fractional parts are discarded.

Logical input is treated similarly.



Median Filter can remove the noise, high frequency components from MRI without

disturbing the edges and it is used to reduce salt and pepper noise. This technique calculates

the median of the surrounding pixels to determine the new demonized value of the pixel. A

median is calculated by sorting all pixel values by their size, then selecting the median value

as the new value for the pixel. The amount of pixels which should be used to calculate the

median.

Example

i3=medfilt2(i3,[3 3]);

i3 is filtered image its return from medfilt2 function.

Noise is like interferences which present as an irregular granular pattern. This

random variation in signal intensity degrades image information. The main source of noise

in the image is the patient's body RF emission due to thermal motion. The whole

measurement chain of the MR scanner also contributes to the noise.

This noise corrupts the signal coming from the transverse magnetization variations of

the intentionally excited spins on the selected slice plane. Four filters in the Enhancement

phase are designed to enhance the appearance of images, primarily by sharpening edges,

corners, and line detail. Several of the new enhancement filters also incorporate a noise-

reduction component.



3.7.1 MEDIAN FILTER

In medical image processing, it is necessary to perform a high degree of noise

reduction in an image before performing high-level processing steps. So the noise can be

removed through Median Filter high frequency components from MRI without disturbing

the edges and it is used to reduce ‘salt and pepper’ noise. This technique calculates the

median of the surrounding pixels to determine the new demonized value of the pixel. A

median is calculated by sorting all pixel values by their size, then selecting the median value

as the new value for the pixel. The amount of pixels which should be used to calculate the

median.

For each pixel, a 3 x 3, 5 x 5, 7 x 7, 9 x 9, 11 x 11 window of neighbourhood pixels

are extracted, and the pixel intensity values are arranged in ascending order and the median

value is calculated for that window. The intensity value of the center pixel is replaced with

the median value. This procedure is done for all the pixels in the image to smoothen the

edges of Magnetic Resonance Image. High Resolution Image was obtained when using 3 x 3

than 5 x 5 and so on. The below table3.2 shows the median filter.

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5 x

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7 x

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9 x

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11

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3.1

1 T

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3x3 w

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x 5

win

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, 7

x 7

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9 x

9 w

ind

ow

,11x 1

1 w

ind

ow

.



Table 3.2 The example of median filter with 3 x 3 windows

(a) Before filtering

42 47 52

55 64 41

47 55 66

41, 42, 47, 47, 52, 55,55,64,66 Ascending Order of pixel

intensity

Median value 52

(b) After Filtering

42 47 52

55 52 41

47 55 66

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EN

HA

NC

EM

EN

T

Tab

le 3

.3 P

erfo

rm

an

ce

An

aly

sis

of

Med

ian

Fil

ter

wit

h d

iffe

ren

t se

t of

win

dow

.

Pix

el s

ize

Mea

n g

ray l

evel

of

fore

gro

un

d

Mea

n g

ray l

evel

of

Back

gro

un

d

Con

trast

valu

e

3×

3

93.1

54

4.0

49

0.9

167

5×

5

95.4

14

4.2

67

0.9

144

7×

7

95.4

75

4.3

05

0.9

137

9 ×

9

94.8

35

4.2

84

0.9

136

11 ×

11

93.8

69

4.2

43

0.9

135

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D S

YS

TE

M F

OR

AU

TO

MA

TIC

DE

TE

CT

IO

N O

F B

RA

IN

TU

MO

R T

HR

OU

GH

MR

I

PR

EP

RO

CE

SS

IN

G &

EN

HA

NC

EM

EN

T

Fig

ure

3.1

2 P

erfo

rm

an

ce

An

aly

sis

of

Med

ian

Fil

ter



The above 3×3, 5×5, 7×7, 9×9, 11×11 windows are analyzed in that 3×3 window is

choose based on the high contrast than 5×5, 7×7, 9×9,and 11×11.

3.7.2 WEIGHTED MEDIAN FILTER

A weighted median filter controlled by evidence fusion is proposed for removing

noise from MRI brain images with contrast. It has a great potential for being used in rank

order filtering and image processing. The weights of the filter are set based on intensity

value of the pixels in the MRI image. Here we used four weights such as 0, 0.1, 0.2 and 0.3.

If the intensity value of the pixel is 0 then consider the weight of the pixel is 0. Else if the

range of pixel intensity between 1-100 then the weight is 0.1, else if the range of pixel

intensity between 101-200 and the weight is 0.2, otherwise the weight of the pixel is 0.3.

The above weights are multiplied with pixel intensity after that the median filter is applied

for calculate weighted median filter. The following figure 3.14 shows the filtered original

MRI brain image using 3×3, 5×5, 7×7, 9×9, 11×11 windows.

CA

D S

YS

TE

M F

OR

AU

TO

MA

TIC

DE

TE

CT

IO

N O

F B

RA

IN

TU

MO

R T

HR

OU

GH

MR

I

PR

EP

RO

CE

SS

IN

G &

EN

HA

NC

EM

EN

T

Tab

le 3

.4 P

erfo

rm

an

ce

An

aly

sis

of

Wei

gh

ted

Med

ian

Fil

ter

wit

h d

iffe

ren

t se

t of

win

dow

Pix

el s

ize

Mea

n g

ray

level

of

fore

gro

un

d

Mea

n g

ray

level

of

Back

gro

un

d

Con

trast

valu

e

3×

3

88.2

121

3.3

551

0.9

267

5×

5

96.4

823

3.6

145

0.9

278

7×

7

95.9

038

3.6

561

0.9

266

9 ×

9

96.1

042

3.7

143

0.9

256

11 ×

11

96.1

785

3.7

485

0.9

250

CA

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YS

TE

M F

OR

AU

TO

MA

TIC

DE

TE

CT

IO

N O

F B

RA

IN

TU

MO

R T

HR

OU

GH

MR

I

PR

EP

RO

CE

SS

IN

G &

EN

HA

NC

EM

EN

T

Fig

ure

3.1

3 P

erfo

rm

an

ce

An

aly

sis

of

Wei

gh

ted

Med

ian

Fil

ter

CA

D S

YS

TE

M F

OR

AU

TO

MA

TIC

DE

TE

CT

IO

N O

F B

RA

IN

TU

MO

R T

HR

OU

GH

MR

I

PR

EP

RO

CE

SS

IN

G &

EN

HA

NC

EM

EN

T

3 x 3

win

dow

5 x

5 w

ind

ow

7 x

7 w

ind

ow

9 x

9 w

ind

ow

11 x

11 w

ind

ow

Fig

ure

3.1

4 T

he

filt

ered

MR

I b

rain

im

age

usi

ng w

eigh

ted

med

ian

fil

ter

wit

h 3

x 3

, 5x 5

, 7x 7

, 9 x

9, 11x 1

1 w

ind

ow



In above table5 3×3, 5×5, 7×7, 9×9, 11×11 windows are analyzed in that 5×5

window is chosen based on the high contrast than 3×3, 7×7, 9×9, and 11×11.

3.7.3 ADAPTIVE FILTER

A new type of adaptive center filter is developed for impulsive noise reduction of an

image without the degradation of an original image. The image is processed using an

adaptive filter. The shape of the filter basis is adapted to follow the high contrasted edges of

the image. In this way the artifacts introduced by a circularly symmetric filter at the border

of high contrasted areas are reduced. The following figure 6 shows the filtered original MR

brain image using 3×3, 5×5, 7×7, 9×9, 11×11 windows.

CA

D S

YS

TE

M F

OR

AU

TO

MA

TIC

DE

TE

CT

IO

N O

F B

RA

IN

TU

MO

R T

HR

OU

GH

MR

I

PR

EP

RO

CE

SS

IN

G &

EN

HA

NC

EM

EN

T

Tab

le 3

.5 P

erfo

rm

an

ce

An

aly

sis

of

Ad

ap

tive

Fil

ter

wit

h d

iffe

ren

t se

t of

win

dow

.

Pix

el s

ize

Mea

n g

ray l

evel

of

fore

gro

un

d

Mea

n g

ray l

evel

of

Back

gro

un

d

Con

trast

valu

e

3×

3

92.5

059

4.2

789

0.9

116

5×

5

95.1

252

4.5

236

0.9

092

7×

7

95.2

662

4.5

717

0.9

084

9 ×

9

94.1

861

4.5

462

0.9

079

11 ×

11

92.5

125

4.4

779

0.9

077

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YS

TE

M F

OR

AU

TO

MA

TIC

DE

TE

CT

IO

N O

F B

RA

IN

TU

MO

R T

HR

OU

GH

MR

I

PR

EP

RO

CE

SS

IN

G &

EN

HA

NC

EM

EN

T

Fig

ure

3.1

5 P

erfo

rm

an

ce

An

aly

sis

of

Ad

ap

tive

Fil

ter

CA

D S

YS

TE

M F

OR

AU

TO

MA

TIC

DE

TE

CT

IO

N O

F B

RA

IN

TU

MO

R T

HR

OU

GH

MR

I

PR

EP

RO

CE

SS

IN

G &

EN

HA

NC

EM

EN

T

3 x 3

win

dow

5 x

5 w

ind

ow

7 x

7 w

ind

ow

9 x

9 w

ind

ow

11 x

11 w

ind

ow

Fig

ure

3.1

6 F

ilte

red

MR

I b

rain

im

age

usi

ng A

dap

tive

filt

er w

ith

3x

3, 5x 5

, 7x 7

, 9x 9

, 11x 1

1



3.7.4 SPATIAL FILTER

A spatial filter design method is used to reduce the magnetic noise in the magnetic

resonance. This filter is used to extract the external magnetic noise appearing on MRI scan

image and to improve the signal-to-noise ratio of the MRI brain image. In spatial domain

filtering, the filter is specified as 3D array. The kernel is then applied to the image via

convolution or correlation using imfilter or filter2. Here, the filter for the picture elements

includes a first filter that applies one filter function to the pixels in each column of the

image. The partially filtered pixels are stored in matrix and then read row by row in a field

interlaced order. The rows of picture elements are sent to a second filter that applies another

filter function to each row. The fully filtered picture elements from the second filter are

stored or converted to a matrix to display an image.

For each pixel, a 3×3, 5×5, 7×7, 9×9, 11×11 window of neighbourhood pixels are

extracted A new type of adaptive center filter is developed for impulsive noise reduction of

an image without the degradation of an original image. The image is processed using an

adaptive filter. The shape of the filter basis is adapted to follow the high contrasted edges of

the image. In this way, the artifacts introduced by a circularly symmetric filter at the border

of high contrasted areas are reduced and the median value is calculated for that window.

Finally 3×3 window is selected for noise reduction based on high contrast. The following

figure 3.18 shows the filtered original MR brain image using 3×3, 5×5, 7×7, 9×9, 11×11

windows.

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M F

OR

AU

TO

MA

TIC

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CT

IO

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RA

IN

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MO

R T

HR

OU

GH

MR

I

PR

EP

RO

CE

SS

IN

G &

EN

HA

NC

EM

EN

T

Tab

le 3

.6 P

erfo

rm

an

ce

An

aly

sis

of

Sp

ati

al

Fil

ter

wit

h d

iffe

ren

t se

t of

win

dow

.

Pix

el s

ize

Mea

n g

ray l

evel

of

fore

gro

un

d

Mea

n g

ray l

evel

of

Back

gro

un

d

Con

trast

valu

e

3×

3

92.5

049

4.2

689

0.9

106

5×

5

95.1

232

4.5

136

0.9

072

7×

7

95.2

552

4.5

617

0.9

024

9 ×

9

94.1

851

4.5

452

0.9

019

11 ×

11

92.5

225

4.4

679

0.9

017

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YS

TE

M F

OR

AU

TO

MA

TIC

DE

TE

CT

IO

N O

F B

RA

IN

TU

MO

R T

HR

OU

GH

MR

I

PR

EP

RO

CE

SS

IN

G &

EN

HA

NC

EM

EN

T

Fig

ure

3.1

7 P

erfo

rm

an

ce

An

aly

sis

of

Sp

ati

al

Fil

ter

CA

D S

YS

TE

M F

OR

AU

TO

MA

TIC

DE

TE

CT

IO

N O

F B

RA

IN

TU

MO

R T

HR

OU

GH

MR

I

PR

EP

RO

CE

SS

IN

G &

EN

HA

NC

EM

EN

T

3 x 3

win

dow

5 x

5 w

ind

ow

7 x

7 w

ind

ow

9 x

9 w

ind

ow

11 x

11 w

ind

ow

Fig

ure

3.1

8 F

ilte

red

MR

I b

rain

im

age u

sin

g S

pati

al

filt

er w

ith

3x

3, 5x 5

, 7x 7

, 9x 9

,11x 1

1



In the above table 3×3, 5×5, 7×7, 9×9, 11×11 windows are analyzed in that 3×3

window is chosen based on the high contrast than 5×5, 7×7, 9×9 and 11×11. The filters in

the Noise Reduction class are designed to remove extreme or outlier values from image

areas that should have relatively uniform values.

3. 8 PERFORMANCE EVALUATION

It is very difficult to measure the improvement of the enhancement objectively. If the

enhanced image can make observer perceive the region of interest better, then the original

image has been improved. In order to compare different enhancement algorithms it is better

to design some methods for the evaluation of enhancement objectively. The statistical

measurements such as variance or entropy can always measure the local contrast

enhancement; however that show no consistency for the MRI. Performance of the Median

filter, Weighted Median filter, Adaptive filter and spatial filters are analyzed and evaluated

using the following equations.

Contrast (C) of MRI = C processed / C original

C = (f-b) / (f + b)

(3.1)

Noise level= standard derivation (σ) of the background

σ = √ (1/N) ∑ (bi-b) 2

(3.2)

bi = Gray level of a background region

N = total number of pixels in the surrounding background region (NB)



The value of PSNR and ASNR can be calculated using the following equations

PSNR = (p-b) / σ

(3.3)

ASNR = (f-b)/ σ

(3.4)

Where

p - is the maximum gray-level value

f - mean gray -level value of the fore ground,

b - mean gray-level value of the background

The value of two indexes are larger where as the enhancement method perform

better. This thesis is based on the experiments and results weighted median filter is highly

contrast than other three filters based on the below statistical analysis from PSNR and

ASNR values. Figure 3.19 (a&b )shows the value of Peak Signal-to-Noise Ratio (PSNR)

filters and the value of Average Signal-to-Noise Ratio (ASNR) filters.


SEGMENTATION

101

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D S

YS

TE

M F

OR

AU

TO

MA

TIC

DE

TE

CT

IO

N O

F B

RA

IN

TU

MO

R T

HR

OU

GH

MR

I

SE

GM

EN

TA

TIO

N

101

Tab

le 3

.7 P

erfo

rm

an

ce

An

aly

sis

of

Fil

ters

S.n

o

Fil

ters

P

SN

R

AS

NR

1

Med

ian F

ilte

r 0.9

1543

0.9

267

2

Wei

ghte

d M

edia

n F

ilte

r 0.9

2667

0.9

278

3

Adap

tive

Fil

ter

0.9

126

0.9

261

4

Sp

atia

l F

ilte

r 0.8

9120

0.8

991

CA

D S

YS

TE

M F

OR

AU

TO

MA

TIC

DE

TE

CT

IO

N O

F B

RA

IN

TU

MO

R T

HR

OU

GH

MR

I

SE

GM

EN

TA

TIO

N

102

Fig

ure

3.1

9 (

a)

Perf

orm

an

ce A

naly

sis

Fil

ters

CA

D S

YS

TE

M F

OR

AU

TO

MA

TIC

DE

TE

CT

IO

N O

F B

RA

IN

TU

MO

R T

HR

OU

GH

MR

I

SE

GM

EN

TA

TIO

N

103

Fig

ure

3.1

9(b

) P

erfo

rm

an

ce A

naly

sis

Fil

ters

CA

D S

YS

TE

M F

OR

AU

TO

MA

TIC

DE

TE

CT

IO

N O

F B

RA

IN

TU

MO

R T

HR

OU

GH

MR

I

SE

GM

EN

TA

TIO

N

104

Fig

ure

3.2

0 P

re-P

roces

sin

g

CA

D S

YS

TE

M F

OR

AU

TO

MA

TIC

DE

TE

CT

IO

N O

F B

RA

IN

TU

MO

R T

HR

OU

GH

MR

I

SE

GM

EN

TA

TIO

N

105

Fig

ure

3.2

1 E

nh

an

cin

g t

he

Image


SEGMENTATION

101

3.9 SUMMARY

This chapter has proposed a gradient based image enhancement method using

first derivative and local statistics and showed the validity of detection of MRI. Initially

the MRI brain image is acquired from MRI brain data set to MATLAB 7.1. After

acquisition the MRI is given to the preprocessing stage, here the film artifacts labels are

removed. Next, the high frequency components and noise are removed from MRI using

the following filters. Such as Median filter, Weighted Median filter, Adaptive filter and

Spatial filter. The Computational result is used to enhance the Image and the

performance of the system was investigated. Finally the best filter of weighted median

filter is identified and used for MR brain image enhancement. It is used for removing

noise from MRI brain images with high contrast. The merit of using Weighted Median

Filter can remove the noise without disturbing the edges.

chapter iii preprocessing &...

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