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Performance Comparison of wavelet family based Bio-Medical Image compression
Mr. Shubham Yadav1, Mrs. Shikha Singh2, Mrs. Akanksha Awasthi3 1M.Tech. Scholar, ECE Department, CVRU, Bilaspur
2Asst.Professor, ECE Department, CVRU, Bilaspur 3Asst.Professor, ECE Department, CVRU, Bilaspur
[email protected], [email protected], [email protected] Abstract- In paper, An attempt has been made to examine and
analyze the compression capability of the different wavelet
family on the bio-medical images. Different wavelet
function are used to produce the compressed bio-medical
images while keeping the PSNR(Peak signal to noise ratio )
constant. A simulation program has been developed under
MATLAB platform to study the above mentioned cause.
Conclusion is drawn on the basis of experimentation
through the simulation program.
Keywords- DCT(Discrete Cosine Transform),DWT(Discrete Wavelet
Transform), JPEG(Joint Picture Expert Group)MRI(Magnetic
Resonance Imaging)
I. INTRODUCTION
The process of enc
The process of encoding or representing the
information or data in lesser bits is called
compression. Compression is advantageous
because it helps us to reduce the storage
requirement of the information or data in storage
devices like hard disk, CD ROM etc.
[1],[2]Compression also help us to send the data
or information through internet by occupying the
lesser bandwidth. With compression, we can use
the storage devices or internet facility in a very
economical ways. On the other hand compression
has some drawbacks or limitation[3],[4].
Compression in the information or data is
achieved against the quality of the information.
Compression rendered the information loss in
quality. One more disadvantage is that it require
decompression scheme for decompressing the
data which require extra hardware and hence
become costly. For example, for compressing the
digital image require an extra hardware to
decompress the data. From this discussion it is
clear that a good compression scheme must be
trade off between the amount of compression,
amount of distortion produced due to the
compression and also the computational time and
resources[5-7].
Image compression is one of the way of data
compression in which the main goal is to reduce
the number bytes using to represent the graphics
image by keeping the distortion in image to the
acceptable level. The reduction in bytes of the
image means reduction in the size of the image
and hence more images can be accommodated in
a given storage devices. Compression also
reduce the bandwidth requirement of the internet
Shubham Yadav et al, International Journal of Computer Science & Communication Networks,Vol 5(5),338-347
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and also the time required to send the information
across the world. Compression in which some
information is lost is known as the lossy
compression while the one in which no
information is lost is known as the lossless
compression. Transform domain coding and
predictive coding comes under the lossy
compression. Transform domain methods are
useful for compression as well as for hiding
secret data[8]
There are many algorithms for compressing the
images. In internet, two most widely used image
compression scheme are JPEG (Joint Picture
Expert Group) and GIF. JPEG scheme is used for
photograph while the GIF scheme of
compression is used for the images where the
geometrical shapes are simple.
Medical imaging as describe earlier require
compression but without the loss of information .
most of the medical images like x-ray images,
MRI images ultrasound images and mammogram
images contain lots of useful information. In
compression generally the high frequency
component is lost. This high frequency
component in medical imaging paly vital role and
carry some useful information therefore medical
images need to be handled carefully when
applying compression algorithm.
This paper is an attempt to analyze the different
wavelets family based compression on medical
imaging and its effect. This work will definitely
helps us to decide which wavelet family is best
suitable for which type of medical imaging [9].
II. RELATED WORK
Compression is a very vast topic and lots of work
has been done in the past for designing a good
compression algorithm this section briefly
describe some of the noteworthy contribution in
this field.
Wavelet and ridgelet can be used for image
compression and the algorithm based on this is
proposed in the literature[10]. Principal
component analysis and wavelet transform in the
form of combination can also be used in tha past
for image compression[11].Sumithra proposed a
multiwavelet transform based image compression
in his paper which require less computational
time[12].
Meenakshi Choudhary suggested a fast haar
wavelet tarsnform based image compression
which is a combination of wavelet transform and
Singular value decomposition(SVD)[13].
Combination of DCT and DWT can also be used
for image compression [14]
Wavelet based image compression method by
using luminance and chrominance component of
the image is also one of the noteworthy
contribution in this field[15]. Combining the
JPEG techniques with the symbol reduction
algorithm can also be used for image
compression[16]. One of the noteworthy
contribution in image compression is also carried
out in a [17] which is based on the combination
of DCT (Discrete Cosine Transform)and
DWT(Discrete Wavelet Transform). Instead of
using the combination of DCT and DWT in RGB
Shubham Yadav et al, International Journal of Computer Science & Communication Networks,Vol 5(5),338-347
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ISSN:2249-5789
space if one use Y,Cb and Cr space then the
combination of the DCT and DWT gives better
result[18]. Some other image compression
techniques which are based on DCT and DWT is
found in [19-25].
III. METHODOLOGY
This work is an attempt to explore the various
wavelet function for compressing the image of
different kind and its effect on the compression
ratio and quality of the image. The Overall block
diagram of the proposed method is shown in the
figure 1. As per the figure 1, first of all a
biomedical image has been taken as a input to
this algorithm. The second block convert the
image in to a gray scale image. This step ensure
that the the image is always a gray scale image to
this system. In the next block image is resize in to
a fixed size. Here we have converted an image in
to size of 256x256 dimension. This is very
important step because later on we have to
comapre all the images. Once all the images are
resized to predefined fixed size then on each
image, wavelet transform is applied using
different wavelet function. The output of this
block is compressed image.
Images are compressed using one level
decomposition and two level decomposition.
This compressed image is then compared with
the original image in term of PSNR(Peak signal
to noise ratio) for quality measure and
compression ratio for finding the amount of
compression achieved.
Figure 1 Block Diagram of Methodology
A. Algorithm Steps for wavelet function based Compression
Step1 Input the image.
Step2 Convert inage in to a gray scale image.
Step3 Resize the image.
Step4 Apply single level/multi level
decomposition using any one wavelet and
obtain all the four frequency band.
Step5 Obtain the book keeping matrix of
coefficient.
Step6 Compute the threshold value for denoising.
Shubham Yadav et al, International Journal of Computer Science & Communication Networks,Vol 5(5),338-347
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ISSN:2249-5789
Step7 Compute the number of significant
coefficient required for reconstruction of the
image faithfully.
Step8 using step 6 and step 7 reconstruct the
image.
Step9 Compute the Peak signal to noise
ratio(PSNR) between original and reconstructed
image.
Step10 Compute the Compression ratio between
original and reconstructed image.
IV. EXPERIMENTAL RESULT
Since the main objective of this project is to decide
which wavelet function is best suitable for a given
bio-medical image, therefore 4 different types of
biomedical images have been taken for the analysis
i. X-ray Image
ii. MRI image
iii. Ultrasound Image
iv. Mammography image
In order to see the compression efficiency of
different wavelet function, first of all we set the PSNR
Figure 2 Flow diagram of image compression
using HAAR wavelet function
value for a particular class of bio0medical image.
After analyzing the compression ratio achieved by
the wavelet function, wavelet function which gives
the highest compression ratio is selected as the best
suitable wavelet function for that kind of medical
image for achieving best compression.
The compression process is carried out in two phase.
In the first phase, first and second level of image
decomposition is performed. In the second phase,
reconstruction of the compressed image is achieved
and compute the threshold for the compression and
the PSNR. The value of the threshold and the PSNR is
set to constant for a particular type of image.
Shubham Yadav et al, International Journal of Computer Science & Communication Networks,Vol 5(5),338-347
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ISSN:2249-5789
The PSNR value is kept to 5.9866.
Table 1 Compression ratio for X-ray images
Image Type
Wavelet Function
Compression Ratio
PSNR (Fixed)
MRI Images
Haar 3.1591. 5.9866.
Daubechies 3.0691 5.9866.
Coiflets 2.7399 5.9866.
Biorthogonal 2.6914. 5.9866.
Figure 3 Graph between various wavelet functions and their compression ratios for x-ray images
Figure 4 contain the second level decomposition
of x-ray images and reconstructed images using
Haar wavelet. Similarly figure 5,6,8 contain the
second level decomposition of the MRI,
ultrasound and Mammogram images and their
reconstructed part.
Here the table 1 contain the compression ratio of
x-ray images for different types of wavelet
families for fixed PSNR. From this table it is
clear that Haar wavelet is best suitable for
compressing the X-ray images.
Table 2 depicts the compression ration for
different tyupes of wavelet for MRI images and
from this table it is clear that for MRI images
best compression ratio is obtained by again Haar
wavelet.
Table 3 present the compression ration for
ultrasound images and from this table it is clear
that Daubechies wavelet is best for this type of
image compression.
Table 4 represent the compression ration for
mammogram images and from this table it is
evident that Coiflets wavelet is best for
compressing the mammogram images.
Figure 4 Second level decomposition of x-ray images using HAAR WT (Upper) and Origianl and second level
reconstructed image (lower) using HAAR.
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ISSN:2249-5789
Figure 5 Second level decomposition of MRI images using Daubechies WT (left) and Origianl and second level reconstructed image (Right) using Daubechies WT.
Table 2 Compression ratio for MRI images
Image Type
Wavelet Function
Compression Ratio
PSNR (Fixed)
MRI Images
Haar 3.5227 5.9866.
Daubechies 2.1582 5.9866.
Coiflets 1.9607 5.9866.
Biorthogonal 1.9608 5.9866.
Figure 6 Second level decomposition of ultrasound images using Coiflets (left) and Origianl and second level reconstructed image (Right) using Coiflets s WT.
Figure 7 Graph between various wavelet functions and their compression ratios for MRI images
Shubham Yadav et al, International Journal of Computer Science & Communication Networks,Vol 5(5),338-347
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ISSN:2249-5789
Figure 8 Second level decomposition of mammogram images using Biorthogonal (left) and Origianl and second level reconstructed image (Right) using Biorthogonal WT.
Table 3 Compression ratio for Ultrasound images
Image Type Wavelet Function
Compression Ratio
PSNR (Fixed)
Ultrasound Images
Haar 3.0063 5.9866.
Daubechies 3.4008 5.9866.
Coiflets 2.8208 5.9866.
Biorthogonal 2.7560 5.9866.
Figure 9 Graph between various wavelet functions and their compression ratios for ultrasound images
Table 4 Compression ratio for Mammogram images
Image Type Wavelet Function
Compression Ratio
PSNR (Fixed)
Mammogram Haar 3.0804 5.9866.
Daubechies 2.4749 5.9866.
Coiflets 3.3726 5.9866.
Biorthogonal 2.0253 5.9866.
Figure 10 Graph between various wavelet functions and their compression ratios for Mammography
images
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ISSN:2249-5789
Peak signal to noise ratio is given by the
following formula
Here
= is the row or column dimension of original
image.
Compression ratio is the measure which is used
to find out how much compression is achieved
using the compression algorithm. It is basically a
ratio of size of original and compressed image
and is given by-
V. CONCLUSION
After exhaustive experiments on different bio-medical images, it can be concluded that all the wavelet function are able to achieve some compression. For X-ray images, wavelet function ‘Haar’ gives the best compression ratio of 3.1591 which is better than the compression ratio of other wavelet function.
As far as MRI images are concerned, it can be comcluded that in MRI images again the wavelet function ‘Haar’ gives the best compression ratio of 3.5227. Compression ration for rest of thye wavelet function is less than this value. So in case of MRI images it will be better to compress the MRI images with Haar wavelet function.
In case of Ultrasound images , the best compression ratio is achieved by using
‘Daubechies’ wavelet function and compression ratio is found to be 3.4008. So it is better to compress the Ultrasound images using ‘Daubechies’ wavelet function.
For Mammogram images, compression ration achieved by the ‘Coiflets’ function is highest as compared to the compression ratio of other wavelet function. So it can be concluded that Mammogram images can be compressed to the maximum level ny wavelet function ‘Coiflets’.
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