www.ijecs.in
International Journal Of Engineering And Computer Science ISSN:2319-7242
Volume 4 Issue 6 June 2015, Page No. 12914-12918
A Comprehensive Review on Image Fusion Techniques
Rajveer Kaur1, Er. Gurpreet Kaur2
1CSE
DEptt., SVIET, Banur, Punjab
rajveerkaur269@gmail.com
2CSE
DEptt., SVIET, Banur, Punjab
Abstract: Medical image fusion is the procedure of enlisting and joining various images from single or different imaging modalities to
enhance the imaging quality and lessen irregularity and repetition with a specific end goal to expand the clinical appropriateness of
medicinal images for analysis and appraisal of restorative issues. Multi-modular medical image fusion calculations and gadgets have
demonstrated remarkable accomplishments in enhancing clinical exactness of choices in view of restorative images. This audit article gives
an accurate posting of techniques and compresses the wide experimental difficulties confronted in the field of restorative image fusion. We
describe the medical image fusion exploration in light of (1) the generally utilized image fusion routines, (2) imaging modalities, and (3)
imaging of organs that are under study. This survey reasons that despite the fact that there exists a few open finished mechanical and
experimental difficulties, the fusion of restorative images has ended up being valuable for propelling the clinical dependability of utilizing
medical imaging for medicinal diagnostics and investigation, and is an exploratory order that can possibly fundamentally develop in the
impending years.
Keywords : Image fusion, medical image, multi-focus image, information fusion, Artificial Neural Network
1. INTRODUCTION
1.INTRODUCTION
The term fusion infers all around an approach to manage
extraction of information got in a couple spaces. The target
of image fusion (IF) is to arrange correlative multi sensor,
multi fleeting and/or multi view data into one new image
containing information the way of which can't be achieved
something else. The term quality, its criticalness and
estimation depend on upon the particular application.
Image fusion has been used as a piece of various application
districts. In remote recognizing and in space science,
multisensor fusion is used to fulfill high spatial and
unearthly resolutions by joining images from two sensors,
one of which has high spatial determination and the other
one high supernatural determination. Different fusion
applications have appeared in restorative imaging like
simultaneous appraisal of CT, MRI, and/or PET images. A
considerable measure of uses which use multisensor fusion
of perceptible and infrared images have appeared in
military, security, and surveillance districts. Because of
multiview fusion, a course of action of photos of the same
scene taken by the same sensor however from different
points of view is merged to get a photo with higher
determination than the sensor regularly offers or to recover
the 3D representation of the scene. The multitemporal
strategy sees two unmistakable focuses. Photos of the same
scene are obtained at assorted times either to find and
evaluate changes in the scene or to get a less degraded photo
of the scene.
Medical imaging offers extraordinary gadgets that help
specialists in the determination process. Today, there are
various remedial modalities that give fundamental
information about unmistakable illnesses. These supplies are
joined by programming undertakings which offer image
taking care of workplaces. Vital information are offered by
these modalities. A valid example, CT gives best
information about denser tissue and MRI offers better
information on fragile tissue, [4]. These complementarities
have incited felt that merging images picked up with
particular restorative devices will make a photo that can
offer more information than each other separate. In this way,
the procured image can be to a great degree significant in
the examination procedure, and that is the reason the photo
fusion has transform into a discriminating investigation
field.
There are some discriminating necessities for the photo
fusion process, [5]:
Rajveer Kaur1 IJECS Volume 4 Issue 6 June, 2015 Page No.12914-12918
Page 12914
-The consolidated image should protect all critical
information from the data images
-The image fusion should not familiarize artifacts which can
lead with a wrong conclusion
Likewise, the use of multi-sensor [7] and multi-source
image fusion methodologies offer a more paramount
contrasts of the segments used for the Medical examination
applications; this routinely prompts capable information
taking care of that can uncover information that is by and
large imperceptible to human eye. The additional
information procured from the consolidated images can be
all around utilized for more correct restriction of
abnormalities.
In the Image Fusion system the great data from each of the
given images is melded to frame a resultant image whose
quality is better than any of the information images. Image
fusion technique can be extensively characterized into two
gatherings i.e Spatial area fusion system Transform space
fusion.
2. LITERATURE REVIEW
Abhinav Krishn et al. (2014) [42]Medical image fusion for
merging of complementary diagnostic content has been
carried out in this paper using Principal Component
Analysis (PCA) and Wavelets.The proposed fusion
approach involves sub-band decomposition using 2DDiscrete Wavelet Transform (DWT) in order to preserve
both spectral and spatial information. Further, PCA is
applied on the decomposed coefficients to maximize the
spatial resolution. An optimal variant of the daubechies
wavelet family has been selected experimentally for better
fusion
results.Simulation
results
demonstrate
an
improvement in visual qualityof the fused image in
comparison to other state-of-art fusion approaches.
Patil, U et al. (2011) [35] has focused on image fusion count
using different levelled PCA. Inventors depicted that the
Image fusion is a method of uniting two or more images
(which are enrolled) of the same scene to get the
additionally illuminating image. Dynamic multiscale and
multiresolution image taking care of routines, pyramid
disintegration are the reason for most of image fusion
computations. Essential part examination (PCA) is a most
likely comprehended arrangement for highlight extraction
and estimation diminish and is used for image fusion. We
propose image fusion count by joining pyramid and PCA
techniques and carryout the quality examination of proposed
fusion figuring without reference image. Patil, U et al.
(2011) [8] has displayed fusion using pyramid, wavelet and
PCA fusion techniques and do execution examination for
these four fusion strategies using particular quality measures
for arrangement of data sets and showed that proposed
image fusion using dynamic PCA is better for the fusion of
multimodal imaged. Clear audit with quality parameters are
used to land at a fusion results.
We Qiang Wang et al. (2004) [40] has analyzed that the
Image fusion is transforming into a standout amongst the
most sizzling methodology all fusion structure, and
subjective fusion structure. Likewise, the effects of such
image fusion structures on the shows of image fusion are
dismembered. In the trial, makers elucidated the normal
hyper unearthly image data set is consolidated using the
same wavelet change based image fusion strategy, however
applying fluctuate rent fusion structures. The refinements
among their merged images are inspected. The exploratory
results attest the theoretical examination that the displays of
image fusion systems are associated with the fusion
estimation, and also to the fusion structures, and
different image fusion structures that produces
various fusion execution even using the same image
fusion procedure.
Desale, R.P et al. (2013) [28] illuminated that the Image
Fusion is a method of merging the critical information from
a course of action of images, into a lone image, wherein the
resultant joined image will be more instructive and complete
than any of the data images. This paper looks at the
Formulation, Process Flow Diagrams and estimations of
PCA (essential Component Analysis), DCT (Discrete
Cosine Transform) and DWT based image fusion routines.
The results are moreover shown in table & image structure
for close examination of above systems. The PCA & DCT
are standard fusion frameworks with various drawbacks,
however DWT based strategies are more extraordinary as
they gives better results to image fusion. In this paper, two
counts considering DWT are proposed, these are, pixel
averaging & most compelling pixel substitution approach.
Aribi, W
et al. (2012) [27] explained that the
quality of the medical image can be surveyed by a
couple subjective techniques. However,
the
objective
technical assessments
of
the
nature
of
medicinal imaging have been starting late proposed. The
fusion of information from unmistakable imaging modalities
allows a more correct examination. We have developed new
techniques in light of the multi determination fusion. Xbeam and PET images have been consolidated with eight
multi determination procedures. For the evaluation of fusion
images got, makers picked by target methodology. The
results exhibited that the fusion with RATIO and contrast
methodologies to offer the best results. Appraisal by target
specific nature of Medical images merged is achievable and
successful.
Haghighat, M et al. (2010) [30] has elucidated that the photo
fusion is a framework to merge information from different
photos of the same scene to pass on simply the
accommodating information. The discrete cosine change
(DCT) based frameworks for image fusion are more suitable
Rajveer Kaur1 IJECS Volume 4 Issue 6 June, 2015 Page No.12914-12918
Page 12915
and productive logically structure. In this paper a gainful
strategy for fusion of multi-focus images considering change
found out in DCT space is displayed. The exploratory results
exhibits the efficiency change of our system both in quality
and many-sided nature diminishing in examination with a
couple recently proposed frameworks.
The K-implies calculation is a divided bunching calculation,
which is utilized to convey focuses in highlight space among
a predefined number of classes. This calculation is
connected to the n-dimensional histogram of the images to
be combined. The calculation could be utilized to use data
from sets of images, for example, CT, SPECT, MRI-T1,
MRI-T2, and useful MRI, to accomplish tissue grouping and
fusion.
3.IMAGE FUSION TECHNIQUES
3.5 The Fuzzy K-Means Algorithm
3.1Fusion Using Logical Operators
This method of fusion data utilizes sensible administrators.
One image is the reference image and it is not prepared.
From the second image is built up a locale of interest and the
data from these images are then joined. The most
straightforward approach to consolidate data from the two
images is by utilizing a legitimate administrator, for
example, the XOR administrator, as indicated by the
accompanying comparison:
This strategy is a variety of the K-implies calculation, with
the presentation of fluffiness as a participation capacity. The
participation capacity characterizes the likelihood with
which every image pixel fits in with a particular class. It is
additionally connected on the n-dimensional histogram of
the images to be intertwined. The FKM calculation and its
varieties have been utilized for comprehending a few sorts
of example acknowledgment issues.
3.6 Fusion to Create Parametric Images
I(x, y) = IA(x,y)(1 –M(x, y))+ IB(x,y)M(x, y) (1)
where M(x, y) is a Boolean cover that checks with 1s each
pixel, which is replicated from image B to the combined
image I(x, y), [21].
3.2 Fusion Using a Pseudo-shading Map
As indicated by this fusion method, the enrolled image is
rendered utilizing a pseudo-shading scale and is
straightforwardly overlaid on the reference image. A
pseudo-shading guide is characterized as a correspondence
of a (R, G, B) triplet to each unmistakable pixel esteem. For
the fusion, we have utilized six hues mapped on the all
grayscale levels in the second image as shown in Figure1.
Figure 1. Pseudo-shading
3.3 Bunching Algorithms for Unsupervised Fusion of
Registered Images (FKM)
SIn this procedure the fusion is acknowledged by preparing
both enlisted images with a specific end goal to create a
melded image with a proper pixel characterization. The
technique utilizes the twofold histogram P(x, y) of the two
enlisted images, which is characterized as the likelihood of a
pixel (i, j) having an estimation of y in image B, given that
the same pixel has an estimation of x in image A:
P(x, y) = P(IB(i, j) = y|IA(i, j) = x) (2)
3.4 The K-Means Algorithm
This technique is helpful for fusion of data from a
progression of images of a dynamic study to arrange tissues
as indicated by a particular parameter. The utilization of this
method prompts a parametric image, which images pixel by
pixel the estimation of the parameter valuable for the
finding. The obliged characterization is performed by
thresholding the parametric image at a fitting level.
CONCLUSION
In this paper, we have examined techniques for image
fusion.We tried the framework execution by distinctive
investigations considering image sizes as same and
different,elapsed time and memory distribution. In spite of
the fact that determination of fusion calculation is issue
ward yet this audit comes about that spatial space give high
spatial determination. Be that as it may, spatial space have
image obscuring issue. The Wavelet changes is the great
procedure for the image fusion give an excellent
otherworldly substance. This paper displays a writing survey
on image fusion systems. t has been reasoned that every
system it implied for particular application and one strategy
has an edge over the other as far as specific application.
REFERENCES
[1] B. V. Dasarathy, A special issue on natural computing
methods in bioinformatics, Information Fusion 10 (3)
(2009) 209.
[2] B. V. Dasarathy, Editorial: Information fusion in the
realm of medical applications-a bibliographic glimpse
at its growing appeal, Information Fusion 13 (1) (2012)
1–9.
[3] B. V. Dasarathy, A special issue on biologically inspired
Rajveer Kaur1 IJECS Volume 4 Issue 6 June, 2015 Page No.12914-12918
Page 12916
information fusion, Information Fusion 11 (2010) 1.
[4] Guihong Qu, Dali Zhang and Pingfan Yan - Medical
image fusion by wavelet transform modulus maxima,
OPTICS EXPRESS, Vol. 9, No. 4 , 2001,
http://www.opticsinfobase.org/DirectPDFAc
cess
[10/03/2008]
[5] Kirankumar Y., Shenbaga Devi S. - Transform-based
medical image fusion, Int. J. Biomedical Engineering
and Technology, Vol. 1, No. 1, 2007 101
[6] Matsopoulos G. K., Delibasis K. K., Mouravliansky N.
A. - Medical Image Registration and Fusion
Techniques: A Review, Advanced Signal Processing
Handbook, CRC Press LLC, 2001
[7] B. V. Dasarathy, A special issue on natural computing
methods in bioinformatics, Information Fusion 10 (3)
(2009) 209.
[8] B. V. Dasarathy, Editorial: Information fusion in the
realm of medical applications-a bibliographic glimpse
at its growing appeal, Information Fusion 13 (1) (2012)
1–9.
[9] B. V. Dasarathy, A special issue on biologically inspired
information fusion, Information Fusion 11 (2010) 1.
[10] M. C. Casey, R. I. Damper, Editorial: Special issue on
biologically-inspired information fusion, Information
Fusion 11 (1) (2010) 2–3.
[11] J. Navarra, A. Alsius, S. S.-Faraco, C. Spence,
Assessing the role of attention in the audiovisual
integration of speech, Information Fusion 11 (1) (2010)
4–11.
[12] C. E. Hugenschmidt, S. Hayasaka, A. M. Peier, P. J.
Laurienti,
Applying
capacity
analyses
to
psychophysical evaluation of multisensory interactions,
Information Fusion 11 (1) (2010) 12–20.
[13] J. Greensmith, U. Aickelin, G. Tedesco, Information
fusion for anomaly detection with the dendritic cell
algorithm, Information Fusion 11 (1) (2010) 21–34.
[14] J. Twycross, U. Aickelin, Information fusion in the
immune system, Information Fusion 11 (1) (2010) 35–
44.
[15] S. Wuerger, G. Meyer, M. Hofbauer, C. Zetzsche, K.
Schill, Motion extrapolation of auditory–visual targets,
Information Fusion 11 (1) (2010) 45–50.
[16] T. D. Dixon, S. G. Nikolov, J. J. Lewis, J. Li, E. F.
Canga, J. M. Noyes, T. Troscianko, D. R. Bull, C. N.
Canagarajah, Task-based scanpath assessment of multisensor video fusion in complex scenarios, Information
Fusion 11 (1) (2010) 51–65.
[17] J.-B. Lei, J.-B. Yin, H.-B. Shen, Feature fusion and
selection for recognizing cancer-related mutations from
common polymorphisms, in: Pattern Recognition
(CCPR), 2010 Chinese Conference on, IEEE, 2010, pp.
1–5.
[18] S. Tsevas, D. Iakovidis, Dynamic time warping fusion
for the retrieval of similar patient cases represented by
multimodal time-series medical data, in: Information
Technology and Applications in Biomedicine (ITAB),
2010 10th IEEE International Conference on, IEEE,
2010, pp. 1–4.
[19] H. Müller, J. K.-Cramer, The Image CLEF Medical
Retrieval Task at ICPR 2010—Information Fusion to
Combine Visual and Textual Information, in:
Recognizing Patterns in Signals, Speech, Images and
Videos, Springer, 2010, pp. 99–108.
[20] Z. R. Mnatsakanyan, H. S. Burkom, M. R. Hashemian,
M. A. Coletta, Distributed information fusion models
for regional public health surveillance, Information
Fusion 13 (2) (2012) 129–136.
[21] Kirankumar Y., Shenbaga Devi S. - Transform-based
medical image fusion, Int. J. Biomedical Engineering
and Technology, Vol. 1, No. 1, 2007 101
[22]Matsopoulos G. K., Delibasis K. K., Mouravliansky N.
A. - Medical Image Registration and Fusion
Techniques: A Review, Advanced Signal Processing
Handbook, CRC Press LLC, 2001
[23] Howell Jones M., ”Wavelets for Medical Image
Fusion”, A Review of Image Fusion Techniques using
Wavelets,
2007
http://www.howelljones.ca/tech/waveletfusi
on.pdf
[11/12/2008]
[24] Hua-mei Chen - Introduction of Image Fusion,
http://ranger.uta.edu/~hchen/CSE6392/Intro
duction%20of%20Image%20Fusion.ppt [10/03/2008]
[25] Sadjadi F. - Comparative Image Fusion Analysis,
http://www.cse.ohio-state.edu/ OTCBVS/05/OTCBVS05-FINALPAPERS/W01_13.pdf [12.03.2008]
[26] Shen
S.
–
Discrete
wavelet
transform
www.csee.umbc.edu/~pmundur/courses/CM SC691M04/sharon-DWT.ppt [ 01/12/2009]
[27] Aribi, Walid, Ali Khalfallah, Med Salim Bouhlel, and
Noomene Elkadri. "Evaluation of image fusion
techniques in nuclear medicine." In Sciences of
Electronics, Technologies of Information and
Telecommunications (SETIT), 2012 6th International
Conference on, pp. 875-880. IEEE, 2012.
[28] Desale, Rajenda Pandit, and Sarita V. Verma. “Study
and analysis of PCA, DCT & DWT based image
fusion techniques." In Signal Processing Image
Processing & Pattern Recognition (ICSIPR), 2013
International Conference on, pp. 66-69. IEEE, 2013.
[29] Ghimire Deepak and Joonwhoan Lee. “Nonlinear
Transfer Function-Based Local Approach for Color
Image Enhancement.” In Consumer Electronics, 2011
International Conference on, pp. 858-865 . IEEE,2011
[30] Haghighat, Mohammad Bagher Akbari, Ali
Aghagolzadeh, and Hadi Seyedarabi. "Real-time
fusion of multi-focus images for visual sensor
networks." In Machine Vision and Image Processing
(MVIP), 2010 6th Iranian, pp. 1-6. IEEE, 2010.
[31] He, D-C., Li Wang, and Massalabi Amani. "A new
technique for multi-resolution image fusion." In
Geoscience and Remote Sensing Symposium, 2004.
IGARSS'04. Proceedings. 2004 IEEE International,
vol. 7, pp. 4901-4904. IEEE, 2004.
[32] Li, Hui, B. S. Manjunath, and Sanjit K. Mitra.
"Multisensor image fusion using the wavelet
transforms." Graphical models and image processing ,
vol. 3,pp. 235-245. IEEE 1997.
[33] Mohamed, M. A., and B. M. El-Den. "Implementation
of image fusion techniques for multi-focus images
using FPGA." In Radio Science Conference (NRSC),
2011 28th National, pp. 1-11. IEEE, 2011.
[34] O.Rockinger. “Image sequence fusions using a shiftinvariant wavelet transform.” In image processing ,
1997 International Conference on, vol. 3, pp. 288-291.
IEEE1997.
[35] Patil, Ujwala, and Uma Mudengudi. "Image fusion
using hierarchical PCA." In image Information
Rajveer Kaur1 IJECS Volume 4 Issue 6 June, 2015 Page No.12914-12918
Page 12917
Processing (ICIIP), 2011 International Conference on,
pp. 1-6. IEEE, 2011.
[36] Pei, Yijian, Huayu Zhou, Jiang Yu, and Guanghui Cai.
"The improved wavelet transforms based image fusion
algorithm and the quality assessment." In Image and
Signal Processing (CISP), 2010 3rd International
Congress on, vol. 1, pp. 219-223. IEEE, 2010.
[37] Prakash, Chandra, S. Rajkumar, and P. V. S. S. R.
Mouli. "Medical image fusion based on redundancy
DWT and Mamdani type min-sum mean-of-max
techniques with quantitative analysis." In Recent
Advances in Computing and Software Systems
(RACSS), 2012 International Conference on, pp. 54-59.
IEEE, 2012.
[38] Sruthy, S., Latha Parameswaran, and Ajeesh P. Sasi.
"Image Fusion Technique using DT-CWT." In
Automation, Computing, Communication, Control and
Compressed Sensing (iMac4s), 2013 International
Conference on, pp. 160-164. IEEE, 2013.
[39] T.Zaveri, M.Zaveri, V.Shah and N.Patel. “A Novel
Region Based Multifocus Image Fusion Method.” In
Digital Image Processing, 2009 International
Conference on, pp. 50-54. IEEE, 2009.
[40] Wang, Qiang, and Yi Shen. "The effects of fusion
structures on image fusion performances." In
Instrumentation
and
Measurement
Technology
Conference, 2004. IMTC 04. Proceedings of the 21st
IEEE, vol. 1, pp. 468-471. IEEE, 2004.
[41]Y-T. Kim. “Contrast enhancement using brightness
preserving
bihistogram
equalisation.”
In
ConsumerElectronics, 1997 International Conference
on, vol. 43, pp. 1-8., IEEE, 1997.
[42] Abhinav Krishn, Vikrant Bhatej, Himanshi, Akanksha
Sahu, “Medical Image Fusion Using Combination of
PCA and Wavelet Analysis”. 978-1-4799-30807/14/$31.00_c 2014 IEEE
.
Rajveer Kaur1 IJECS Volume 4 Issue 6 June, 2015 Page No.12914-12918
Page 12918