Multimodality medical image fusion using improved contourlet transformation

International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN
0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 2, March – April (2013), © IAEME
383
MULTIMODALITY MEDICAL IMAGE FUSION USING IMPROVED
CONTOURLET TRANSFORMATION WITH LOG GABOR FILTERS
Jaspreet Kaur1
, Chirag Sharma2
1
(Computer science and Engg., Lovely Professional University, Jalandhar, India)
2
(Computer science and Engg., Lovely Professional University, Jalandhar, India)
ABSTRACT
Image fusion is the latest concept in Digital image processing. It is a process of
combination of two images to produce single image that provide more information. In order to
diagnose the disease in medical field, image fusion is one of the best concept that is highly
used. Multimodality medical image fusion is an advance technology that is used by doctors to
diagnose the particular disease. Most important consideration in image fusion: images to be
fused should be registered images i.e. multiple images have same alignment. Main issue in
multimodality medical image fusion is how to fuse two or more images of different modalities,
so that we get more clear and accurate information. In this paper, we proposed improved
contourlet transformation technique with modifying DFBs using log gabor filters will not only
provide good quality of images but also localizes an image more accuratly and minimizes the
DC component(noise in image).
Keywords: CNT, DFBs, Log Gabor Filter, Multimodalities, Medical images fusion.
I. INTRODUCTION
An image fusion can be defined as a process of combining two or more images in one
single image without any loss of information. Main objective of image fusion is to merge the
complementary and redundant information from different images into one fused images which
results in more accurate and clear information. This information can be used for different
purposes like human visual and machine perceptions, diagnosis, analysis. Basically there are
two image fusion systems. First, Single sensor image fusion system in which we use one sensor
that capture multiple images and then fusion them into one image. Imaging sensor has limited
capability and does not work in bad environment like clicking an image in fog and rain. Second,
INTERNATIONAL JOURNAL OF ELECTRONICS AND
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ISSN 0976 – 6464(Print)
ISSN 0976 – 6472(Online)
Volume 4, Issue 2, March – April, 2013, pp. 383-389
© IAEME: www.iaeme.com/ijecet.asp
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384
Multisensor image fusion system in which we use multiple sensor devices mainly we use
infrared cameras which has capability to work in bad environmental conditions. There are three
basic levels of image fusion. Pixel level image fusion is at lowest level and contains detailed
information. Feature level we simply extract features of different images that are to be fused
and then fuse that features to get new image and at last decision level which consist of compact
data. Decision level fusion is effective for complicated systems with multiple true or false
decisions but not suitable for general applications. All decision and control are decided
according to the result of decision level image fusion [1].
1. Multimodality Medical Image Fusion
Medical images are used by doctors to get information about the patient body and to
detect any disease. There are several types of diagnostic tools available to capture an image of
patient’s body. Different types of medical images like X rays which is used to detect broken
bones. CT (computer tomography) provide us structure of bones in patient body. These images
are used to provide us detail information regarding body parts. MRI is used to get information
about soft tissues, organs in human body. It does not use X rays and radiations that harm to our
body. Ultrasound is a safe and less harmful image processing, in which high frequency sound
waves are used.PET that stands for positron emission tomography. Medical imaging is an
important term in field of research, diagnosis and treatment. Multimodality medical image
fusion is a process to merge images that are captured by different modalities such as CT, MRI
and PET and which results in more useful information for diagnosis or doctors regarding
patient’s body. Various fusion techniques has attracted more researchers in this domain to
assist the physicians in fusing and retrieving information from different modalities such as CT,
MRI, PET, SPECT and so on[4].
2. Image Fusion Techniques
Image fusion techniques can be categories into two domains: spatial domain and
frequency domain. In spatial domain we covert an image in the form of matrix and then process
it. In frequency domain images are represented in form of signal or sub bands. Here in this
paper, we are working in transform domain. Further there are three main types which are
discussed below:
2.1 Laplacian Method
In this method, input image is decomposed in form of pyramids. Decomposed image is
represented into two pyramids: First, Smoothing pyramid contains the average pixel values.
Second, Difference pyramid contains the pixel differences that are edges. This pyramid is also
known as multi resolution edge representation of the input image [6]. The basic idea behind this
is to perform a pyramid decomposition on each source image, then integrate all these
decompositions to form a composite representation, and finally reconstruct the fused image by
performing an inverse pyramid transform.
2.2 Wavelet Transformation
Wavelet transform has been greatly used successfully in many areas, such as texture
analysis, data compression, feature detection, and image fusion [5].Wavelet transformation is
basically used to decompose an input image into sub bands. We can apply this transformation
in both spatial and frequency domain. But it is always preferred to apply wavelet in frequency

385
domain in which images are decomposed into frequency sub bands. Multi resolution analysis is
done in wavelet transform.
LL2
LH2
LH1
HL2
HH2
HL1
HH1
Fig. 1 Wavelet Decomposition
In this given figure, 1,2 denote decomposition levels. H denotes High Frequency Bands. L
denotes Low Frequency Bands.
2.3 Contourlet Transformation
Contourlet transformation is based on pyramid method. It is recently developed
transformations used to decompose an image. This method implemented in two stages, first
stage is transformation and second stage sub band Decomposition [4]. Image is decomposed
into different scale by laplacian pyramids and then DFBs are applied to each decomposed
images. An image gets divided into low pass and high passes sub bands. Filtering of these
bands is done by directional filter banks (DFBs).This is a part of a multi-dimensional signal
processing theory called as Multi scale Geometric Analysis (MGA) Tools [4].
3. LOG GABOR FILTER
The “Gabor Filter Bank” is a popular technique used to determine a feature domain for
representing an image [3]. The main limitation of Gabor filter is that, it was designed for a
bandwidth maximum of 1 octave. So, Field introduced Log Gabor Filter to overcome this
limitation. This filter is used for localizing the spatial and frequency information of an image. A
Log Gabor Filter has no DC component and can be constructed with any arbitrary bandwidth.
Two important characteristics of Log-Gabor filter, First, the Log-Gabor filter function always
has zero DC components which not only improve the contrast ridges but also edges of images.
Second, the Log Gabor filter function has an extended tail at the high frequency end which
allows it to encode images [3].Log Gabor Filter use logarithmic transformation which
eliminates the DC component allocated in medium and high pass filters. We use Fourier
transformation in log Gabor filter. This transformation decomposes an image into sin and
cosine component. Major applications of FT (Fourier transformation) are image compression,
image analysis and image filtering.
G(ρ,θ,p,k) = exp(-1/2(ρ-ρk/σρ)2
)exp(-1/2(θ-θk,p/σθ)2
)

386
Log Gabor filters consist of complex-filtering arrangement in p oritentations and k scales,
whose expression in the Log-polar Fourier domain is given in above equation. In this, (ρ, θ) are
the log-polar coordinates and (σρ,σθ) are the angular and radial bandwidths(common for all the
filters).p and k represent the orientation and scale selection respectively.[7]
II. PROPOSED SCHEME
In this paper, we proposed an efficient fusion technique based on contourlet
transformation, in which basic assumption is, medical images which are going to fused should
be registered to assure that corresponding pixels of images are aligned or similar. Algorithm is
given below:
STEP 1: Select two images
STEP 2: Apply multi scale decomposition and Directional Filter Banks over registered images.
STEP 3: Apply multi scale decomposition with Log Gabor filters (modified DFBs) for both
images.
STEP 4: Merge the frequencies.
STEP 5: Resultant single fused image.
STEP 6: Calculate PSNR and Standard Deviation for fused image.
III. EXPERIMENTAL RESULTS
Fig. 2 GUI for Medical Image Fusion

387
Fig. 3 Fusion of medical images using contourlet transformation
Fig. 4 Fusion of medical images using improved contourlet transformation

388
IV. RESULTS AND CALCULATIONS
After the experiments performed on registered Images in MATLAB, we have realized
that above factors determine the quality of fused image. Our technique is better than those of
other techniques of image fusion, because this technique provides high quality of information.
The quality of image is measured by three factors which are given below:
PSNR computes Peak Signal to Noise Ratio, in decibels, between two images. This ratio is
used to provide the quality measurement between two resultant fused images. Efficiency of
particular technique is calculated by calculating PSNR value. Higher the PSNR more will be
the quality.
PSNR =
2
1010log 255 / MSE
Where MSE is calculated as:
MSE =
1
0
,
, 0
1
2
([ ( , ) ( , )] / *
M N
i j
OI i j DI i j M N
− −
= =
−∑ [9]
Where OI is fused image with contourlet transformation,DI is fused image with improved
contourlet transformation, M*N is number of pixels. More the value of MSE lower the value of
PSNR.
SD (Standard deviation) is used to measure the contrast in fused image. An image with high
contrast would have high Standard deviation value.
STD =
=1 =1
21/ ( ( , ) )
M N
m n
MN F m n MEAN−∑∑
Where M*N denotes the size of an image and f(m,n)indicates the gray-value of the pixel of
image f at position(m,n) and
MEAN =
=1 =1
1/ ( , )
M N
m n
MN F m n∑∑ [8]
Table1. Calculation of PSNR, Standard deviation and Quality index values by applying
contourlet and improved contourlet transformation.
S.
NO.
Technique PSNR Standard
Deviation
Quality Index Fused image
1 Contourlet
transformatio
n
29.6515 55.367 71.6595
2 Improved
Contourlet
transformatio
n
34.2325 78.9019 111.1285

389
V. CONCLUSION
Our proposed scheme is applied on medical image fusion. There are number of
techniques have been proposed for image fusion; like wavelet transformation, laplacian
pyramid method, contourlet transformation, curvelet transformation. The proposed fusion
technique is evaluated on the multimodality medical images to fuse them with better quality so
that there is no loss of information. And can be send to doctors without any loss of information
with better quality. From series of experiments we have concluded that our proposed technique
is yielding higher PSNR value then other techniques mentioned above.
REFERENCES
[1] Mo Tingting, Shi Yuehua, Wei Yipeng, ZhaoFeng, ZhuYongxin,(2009),”Implementing
Contourlet Transform for Medical Image Fusion on Heterogenous Platform”, IEEE, Pp
115-120.
[2] Thanushkodi K., Umaamaheshvari A. ,(2010),“Image fusion techniques”, IJRRAS 4 (1).
[3] R. Redondo, F. Šroubek, S. Fischer, G. Cristóbal, (2008),“Multifocus image fusion using
the log-Gabor transform and a Multisize Windows technique”
[4] Kavitha S, Rajkumar S, (2009),“Redundany Discrete Wavelet Transform and Contourlet
Transform for Multimodality Medical Image Fusion with Quantitative Analysis”,IEEE Pp
134-139
[5] Fang Zhijun, Huang Shuying, Park Dong Sun, Yang Yong, Wang
Zhengyou,(2009),”Wavelet based Approach for Fusing Computed Tomography and
Magnetic Resonance Images”, Chinese Control and Decision Conference (CCDC 2009) Pp
5770-5774.
[6] Singh Nupur , Tanwar Pinky,(2012),”Image fusion using improved Contourlet Transform
Technique”,International Journal of Recent Technology and Engineering (IJRTE) ISSN:
2277-3878, Volume-1, Issue-2,Pp 131-139.
[7] Kaur Jaspreet , Sharma Chirag (2012)”An Efficient technique of multimodality medical
image fusion using contourlet transformation”, (IJITEE), Vol 2 ,issue 1,Pg 28-30.
[8] Das Sudeb, Kundu Kumar Malay (2011)”Fusion of Multimodality Medical images using
Combined Activity level measurement and Contourlet Transformation.”(ICIIP),
[9] Saaid Monir Mary,Nossair Bassyouni Zaki,Hanna Tawfik Magdy(2011)”An image
watermarking scheme based on Multiresolution Analysis”,(IEEE).
[10] R. Edbert Rajan and Dr.K.Prasadh, “Spatial and Hierarchical Feature Extraction Based
on Sift for Medical Images”, International journal of Computer Engineering & Technology
(IJCET), Volume 3, Issue 2, 2012, pp. 308 - 322, ISSN Print: 0976 – 6367, ISSN Online:
0976 – 6375.
[11] John Blesswin, Rema and Jenifer Joselin, “A Self Recovery Approach using Halftone
Images for Medical Imagery System”, International journal of Computer Engineering &
Technology (IJCET), Volume 1, Issue 2, 2010, pp. 133 - 146, ISSN Print: 0976 – 6367,
ISSN Online: 0976 – 6375.
[12] Mayur V. Tiwari and D. S. Chaudhari, “An Overview of Automatic Brain Tumor
Detection from Magnetic Resonance Images”, International Journal of Advanced Research
in Engineering & Technology (IJARET), Volume 4, Issue 2, 2013, pp. 61 - 68, ISSN Print:
0976-6480, ISSN Online: 0976-6499.

Multimodality medical image fusion using improved contourlet transformation

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Multimodality medical image fusion using improved contourlet transformation