THE EFFECT OF IMPLEMENTING OF NONLINEAR FILTERS FOR ENHANCING MEDICAL IMAGES USING MATLAB

International Journal of Computer Science & Information Technology (IJCSIT) Vol 7, No 6, December 2015
DOI:10.5121/ijcsit.2015.7605 61
THE EFFECT OF IMPLEMENTING OF NONLINEAR
FILTERS FOR ENHANCING MEDICAL IMAGES USING
MATLAB
Mohamed Y. Adam1
, Dr Mozamel M. Saeed2
, Prof. Dr Al Samani A. Ahmed3
1
king Saud University, TrainingandCommunity Service Center, KSA
2
prince Sattam Bin Abdul-Aziz University, collage of science, Dept. of computer science,
KSA
3
Al Neelain University collage of computer science & information technology, Dept. of
computer science, Sudan
ABSTRACT
Although this huge development in medical imaging tools, we find that there are some human mistakes in
the process of filming medical images, where some errors result in distortions in the image and change
some medical image properties which affect thedisease diagnosis correctly.Medical images are one of the
fundamental images, because they are used in the most sensitive field which is a medical field. The
objective of the study is to identify the effect of implement non-linear filters in enhancingmedical images,
using the strongest and most popular program MATLAB, and because of its advantages in image
processing. After implementation the researcher concluded that we will get the best result for medical
image enhancement by using median filter which is one of the non-linear filters,non-linear filters
implemented using Matlab functions.
KEYWORDS
median filter, noise reduction, maximum filter, minimum filter, enhancement, non-linear filters.
1. INTRODUCTION
Digital Image Processing means using digital computers to correct the appearance of digital
images, correcting the appearance include many activities for example remove noise, smoothing
or sharpening the image, improve the contrast of the image, remove blurring which occurred
during image acquisition.There are many filter techniques, which use to enhance images, and the
enhancement is focus of improving the quality of the image or to obtain certain details. Also
filtering can be used to remove unwanted noise. Certainly in medical image we find salt and
pepper noise.
There is a big collection of functions in MATLABtoolboxthatuse to implement different types of
filters. Some of them are needed in the process of image adjustments and others are responsible
for object recognition. Examples of linear and nonlinear filter including their effects are presented
in Table1. (Ozimek, lectures from Digital image processing, 2010d)

62
Category Filter Effect
LINEAR
High-pass filter
Sharpening
Underlining contours
Low-pass filter
Reduction of the noise,
Smoothing out
Laplace's filter Sharpening
Edge detection filter
Detection of all directions
Edges and corners
Corner detection
filter
NON-
LINEAR
Median filter
Noise reduction without
Blur effect
Minimum filter
Decrease the brightness of
the edge objects
Maximum filter
Increase the brightness of
the edge objects
Table 1. Examples of image enhancing filters
1. IMAGE PROCESSING TECHNIQUE
The main categories of digital image processing techniques include
• Image generation
• Image enhancement
• Image restoration.
Image generation is the most popular digital image processing techniques that use to convert an
image into some sort of ordered layout. For example, the reflection created when scanner pick up
an image, by digitizing this reflection the result is a series of pixels. By the result the generation
techniques help project and recognize a scanned image.
Image enhancement is the process that applied to an image so that the result is more suitable than
the original for a specific application.The image properties and the image source will be taking
into account while enhancement process, so the techniques that use to enhance x-ray may not be
suitable for other kind of image.
Image restoration is an area that also deals with improving the appearance of an image. So
enhancement and restoration both are using to improve image appearance, but the image
restoration is objective while image enhancement is a subjective process.
2. IMAGE ENHANCEMENT TECHNIQUE
The main objective of image enhancement is to improve the quality of the image.Certainly in
medical images we will face with more of the following problems:
• The resolution is low.
• Noise high level.
• Contrast is low.

63
• geometric deformations;
•presence of imaging artifacts.
The imperfections above can be causedwhile filming the imagefor example X-rays may be low
contrast for soft tissues, ultrasound produces very noisy images, and metallic implants willcause
imaging artifacts in MRI or the result of a deliberate trade-off during acquisition. But in this study
we are focus on the image processing only and not be concerned with the challenging problem of
designing optimal procedures for their acquisition
a. NONLINEAR FILTERING
There are a number of techniques used to remove distortions of images, where the techniques of
nonlinear filters is the most important techniques for their properties, so it does not change the
properties of the image but remove distortions to enhance image clarity and effectiveness. The
linear filters rely on identifying noise and then removed and replaced by the neighborhood points
according to the standard, while other points remain unchanged. The most important nonlinear
filters aremedian filter, minimum filter, and maximum filter, and each one of these filters
characteristics and advantages
b. MEDIAN FILTERING
Median filter is one of the most important from nonlinear filters, it is the popular technique which
use to remove noise certainly the impulsive-type noise, and by the way it has the ability of
preserving image details. The median filter rely on identifying noise and then removed and
replaced by the median of neighborhood pixels, while other points remain unchanged. The
median pixel means the middle pixel value witch considered after sorting all pixel values from
surrounding neighborhood pixels numerically.
In median filter, the pixel value of a point p is replaced by the median of pixel value of 8-
neighbourhood of a point ‘p’. Therefore, we can generate the median filter by the following
function:
g p = median{f p , where p ∈ N p } (1)
• The median value will replace the central pixel according to brightness of the neighbouring
pixels.
• When some individual pixels have outliers values, noise will raise out such as shot noise or
impulse noise, So median filter can do a good job of removing such noises.
As we can see in figure 1 blow, we can calculate the median value of pixel neighbourhood of
150, and then is replaced with the median of surrounding pixels value that is 124. Here we use
A3x3 square neighbourhood

64
Figure 1. Neighbourhood values
Neighborhood values:
115, 119, 120, 123, 124, 125, 126, 127, 150
Median value: 124
The advantages of median filter:
–Since output values available from the present neighborhood, the contrast will not be change.
–Boundaries dose not shift in median filtering, that can happenwith other filters.
–The outliers values will be removed, because the median is less sensitive to the extreme values
than the mean.
c. MINIMUM FILTER
The most effective filter that use to remove positive outline noise is minimum filter, and it'suse
also in the computational of binary morphological erosion. Minimum filter defined as:
Minimum A = min A x + i, y + j ,
Where the coordinate x + I, y + j is defined over the image A and the coordinate I, j is defined
over the mask M. The mask M determines which pixels are used in the minimum calculation.
In order to find the darkest points in an image:
First, find the minimum value in the area encompassed by the filter and then reduces the salt
noise because of the minimumoperation;finally, the 0th percentile filter is min filter.
d. MAXIMUM FILTER
The most effective filter that use to remove negative outline noise is maximum filter, and it's use
also in the computational of binary morphological dilation. maximum filter defined as:
Maximum A = max A x + i, y + j ,
Where the coordinate x + I, y + j is defined over the image A and the coordinate I, j is defined
over the mask M. The mask M determines which pixels are used in the minimum calculation.

In order to find the brightest points in an image:
First finds the maximum value in the
noise because of the maximum operation, finally t
3. RESULTS
The proposed methods have applied on different medical images. Some results are illustrated
below. In each figure, we will see the origin
minimum filter and noisy image filtered by maximum filter and
filter in both tow kinds of images.
Figure 2. Original bone image
Figure 4.Bone image filtered by minimum filter
he brightest points in an image:
maximum value in the area encompassed by the filter, and then reduces the pepper
operation, finally the 100th percentile filter is max filter.
we will see the original image, noisy image, noisy image filtered by
noisy image filtered by maximum filter and noisy image filtered by median
filter in both tow kinds of images.
Figure 2. Original bone image Figure 3. Noisy bone image
Figure 4.Bone image filtered by minimum filter Figure 5.Bone image filtered by maximum filter
65
educes the pepper
he 100th percentile filter is max filter.
image, noisy image, noisy image filtered by
image filtered by median
Figure 3. Noisy bone image
Figure 5.Bone image filtered by maximum filter

Figure
Figure 7.Original chest image
Figure 9. Noisychest image filtered by minimum
filter
Figure 6.Bone image filtered by median filter
Figure 7.Original chest image Figure 8.Noisychest image
Figure 9. Noisychest image filtered by minimum Figure 10.Noisychest image filtered by maximum
filter
66
Figure 8.Noisychest image
Figure 10.Noisychest image filtered by maximum

Figure 11
4. CONCLUSION
In the field of medical image, generally
image input devices. Noises can
techniques remove noise from images so image processi
Therefore image filtering is the main
main aim is to remove the impulse
can see in the results aboveafter implementation,
removing noises of medical images.
5. APPENDEX
k = imread('ches.jpg');
k = rgb2gray(k);
subplot(2,2,1);imshow(
g = imnoise(k,'salt& pepper'
subplot(2,2,2);
imshow(g);title('Noisy Image'
B=zeros(size(g));
modifyA=padarray(g,[1 1]);
a=[1:3]';
b=[1:3]';
forx= 1:size(modifyA,1)
fory=1:size(modifyA,2)
mm=reshape(modifyA(x+a
B(x,y)=min(mm);
end
end
B=uint8(B);
subplot(2,2,3),imshow(B),title(
k = imread('ches.jpg');
k = rgb2gray(k);
subplot(2,2,1);imshow(k),title(
n = imnoise(k,'salt& pepper'
subplot(2,2,2);
11.Noisychest image filtered by median filter
generally various noises is added in image while acquiring from
oises can change the properties or generate false output. Image filtering
techniques remove noise from images so image processing algorithm can work effectively.
the main step in most image processing algorithm. In this paper the
main aim is to remove the impulse noise from the medical image using non-linear filters. As we
fter implementation, the median filter has the best results of
removing noises of medical images.
);
subplot(2,2,1);imshow(k),title('ORIGINAL IMAGE');
'salt& pepper');
'Noisy Image')
,[1 1]);
= 1:size(modifyA,1)-2
=1:size(modifyA,2)-2
a-1,y+b-1),[],1);
);
subplot(2,2,3),imshow(B),title('IMAGE AFTER MIN FILTERING'
);
subplot(2,2,1);imshow(k),title('ORIGINAL IMAGE');
'salt& pepper');
67
is added in image while acquiring from
or generate false output. Image filtering
ng algorithm can work effectively.
In this paper the
linear filters. As we
best results of
'IMAGE AFTER MIN FILTERING');

68
imshow(n);title('Noisy Image')
B=zeros(size(n));
modifyA=padarray(n,[1 1]);
a=[1:3]';
b=[1:3]';
forx= 1:size(modifyA,1)-2
fory=1:size(modifyA,2)-2
mx=reshape(modifyA(x+a-1,y+b-1),[],1);
B(x,y)=max(mx);
end
end
B=uint8(B);
subplot(2,2,3);imshow(B),title('IMAGE AFTER MAX FILTERING');
I = imread('ches.jpg');
subplot(2,2,1);
imshow(I);
title('Original Image')
J = imnoise(I,'salt& pepper',0.02);
subplot(2,2,2)
subimage(J)
title('Noisy Image')
L = medfilt2(J,[3 3]);
subplot(2,2,3);
imshow(L)
title('Noisy Image filtered by median filter')
REFERENCES
[1] D.A. Forsyth and J. Ponce, Computer Vision – A Modern Approach, Prentice Hall, 2003
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1993
[4] Image Processing using Matlab. Second Edition. United States of America. Gatesmark Publishing.
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http://www.ijcst.org/Volume6/Issue1/p2_6_1.pdf
[6] Mrozek, Bogumiła – Mrozek, Zbigniew 2001. Matlab 6; Poradnikużytkownika.
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