PCA & CS based fusion for Medical Image Fusion

IJMTST | International Journal for Modern Trends in Science and Technology
Volume 1, Issue 2, October 2015 33
PCA & CS based fusion for Medical Image Fusion
Satish Babu Nalleboina P.Murali Krishana
PG Scholar Assistant Professor
Layola Institute of Technology and Management Layola Institute of Technology and Management
Abstract - Compressive sampling (CS), also called
Compressed sensing, has generated a tremendous amount of
excitement in the image processing community. It provides an
alternative to Shannon/ Nyquist sampling when the signal under
acquisition is known to be sparse or compressible. In this paper,
we propose a new efficient image fusion method for compressed
sensing imaging. In this method, we calculate the two
dimensional discrete cosine transform of multiple input images,
these achieved measurements are multiplied with sampling
filter, so compressed images are obtained. we take inverse
discrete cosine transform of them. Finally, fused image achieves
from these results by using PCA fusion method. This approach
also is implemented for multi-focus and noisy images.
Simulation results show that our method provides promising
fusion performance in both visual comparison and comparison
using objective measures. Moreover, because this method does
not need to recovery process the computational time is
decreased very much.
Keywords: Compressive Sensing, Image Fusion, Multi-Focus
Images, Multi-Focus and Noisy Images
I. INTRODUCTION
Image fusion is defined as a process of combining two or
more images into a single composite image. The single output
image contains the better scene description than the
individual input images. The output image must be more
useful for human perception or machine perception. The basic
problem of image fusion is determining the best procedure for
combining multiple images. The main aim of image fusion is
to improve the quality of information of the output image.
The existing fusion algorithms and techniques shows that
image fusion provide us an output image with an improved
quality.
Only the objects of the image with in the depth of field of
camera are focused and the remaining will be blurred. Fusion
can be defined as the process of combining multiple input
images into a smaller collection of images, usually a single
one, which contains the relevant and important information
from the inputs. Nowadays, many well-known fusion
algorithms have been proposed. But most of them are based
on the whole acquisition of the source images. A work
demonstrated the possibility of fusing images without
acquiring all the samples of the original images, if the images
are acquired under the new technique – compressed sensing.
The noise should be removed prior to performing image
analysis processes while keeping the fine detail of the image
intact. Salt and pepper noise in an image are small, unwanted
random pixels in areas where the surrounding majority of
pixels are a different value, i.e. a white pixel in a black field
or a black pixel in a white field. Many algorithms have been
developed to remove salt and pepper noise in document
images with different performance in removing noise and
retaining fine details of the image. Median filter is a well
known method that can remove this noise from images. The
removal of noise is performed by replacing the value of
window center by the median of center neighbourhood.
Fig 1 General procedure of MSD-based image fusion
Image fusion can be performed at three different levels,
i.e., pixel/data level, feature / attribute level, and
symbol/decision level, each of which serves different purposes
Compared with the others, pixel-level fusion directly
combines the original information in the source images and is
more computationally efficient. According to whether
multistage decomposition (MSD) is used, pixel-level fusion
methods can be
Classified as MSD-based or non-MSD based. Compared
to the latter, MSD-based methods have the advantage of
extracting and combining salient features at different scales,
and therefore normally produce images with greater
information content.
The general procedure of MSD-based fusion is
illustrated in Fig. 1. First, the source images are transformed
to multi scale representations (MSRs) using MSD. An MSR
is a pyramidal structure with successively reduced spatial
resolution; it usually contains one approximation level (APX)
storing low-pass coefficients and several detail levels (DETs)
storing high-pass or band pass coefficients. Then, a certain
fusion rule is applied to merge coefficients at different scales.
Finally, an inverse MSD (IMSD) is applied to the fused MSR
to generate the final image.

Fig 2 T1W and T2W M.R.I Scanning
Two directions can be explored in MSD-based fusion to
enhance the fusion quality: advanced MSD schemes and
effective fusion rules. Here, we focus on the latter and propose
a novel cross-scale (CS) fusion rule, where the belongingness/
membership of each fused coefficient to each source image is
calculated. Unlike previous methods, our fusion rule
calculates an optimal set of coefficients for each scale taking
into account large neighbourhood information, which
guarantees intra scale and inter scale consistencies, i.e.,
coefficients with similar characteristics are fused in a similar
way are avoided in the results.
II. COMPRESSED SENSING
In this section we present a brief introduction to the
Sparse model and compressive sensing background. Consider
a real-valued, finite-length, one-dimensional, discrete-time
signal X , which can be viewed as an N × 1 column vector in
N R with elements x[n], n = 1, 2, . . . , N. Any signal in N R
can be represented in terms of a basis of N × 1 vectors {ψ i } .
The signal X is K-sparse if it is a linear combination of
only K basis vectors, that is, only K of the i S coefficients in
are nonzero and (N − K) are zero. The case of interest is
when K<< N. The signal X is compressible if the
representation has just a few large coefficients and many
small coefficients.
III. IMAGE FUSION METHOD
Our method for two input source images and then we can
generalize it for multiple input images. We compute two-
dimensional discrete cosine transform of two input images
and then these measurements are multiplied with sampling
filter, so compressed images are obtained. We take inverse
discrete cosine transform of them.
Fig 3 Block diagrams of computing
PCA is a statistical method for transforming a
multivariate data set with correlated variables into a data set
with new uncorrelated variables. For this purpose search is
made of an orthogonal linear transformation of the original
N-dimensional variables such that in the new coordinate
system the new variables be of a much smaller dimensionality
M and be uncorrelated. In the Principal Component Analysis
(PCA) the sought after transformation parameters are
obtained by minimizing the covariance of error introduced by
neglecting N-M of the transformed components.
The contrast sensitivity function of luminance shows
band pass characteristics, while the contrast sensitivity
functions of both red–green and yellow–blue show low-pass
behaviour. Therefore, luminance sensitivity is normally
higher than chromatic sensitivity except at low spatial
frequencies. Hence, the fused monochrome image, which
provides combined information and good contrasts, should be
assigned to the luminance channel to exploit luminance
contrast.
Fig 4 Graph 1
In addition, the colour-fused image should also provide
good contrasts in the red–green and/or yellow–blue channels
in order to fully exploit human colour perception. To achieve

this, we can consider that red, green, yellow, and blue are
arranged on a colour circle as in, where the red– green axis is
orthogonal to the yellow–blue axis and colour (actually its
hue) transits smoothly from one to another in each quadrant.
Fig 5 graph 2
IV. SIMULATION RESULTS
The performance of our CS fusion rule was evaluated on
volumetric image fusion scans using both synthetic and real
data. After this validation, we demonstrate the capability of
our fusion rule to fuse other modalities. In addition, we have
consulted a neurosurgeon and a radiologist. In their opinion,
our method not only provides enhanced representations of
information, which is useful in applications like diagnosis
and neuro navigation, but also offers them the flexibility of
combining modalities of their choice, which is important
because the data types required are normally application
dependent.
Figure 6. (a) CT image. (b) MRI image. (c) Fusion result by FAR method. (d)
Fusion result by DS_MS method . (e) Fusion result by Han’s method . (f) Fusion
result by our method
With regard to the visual comparison of the second
group, the fusion result by using our method in Fig. 3 (f)
contains more information than the input images in Fig.3 (a)
and (b). Noise sensitivity is a common concern for many
pixel-level fusion methods. For noisy images, one could
employ a denoising activity-level measure or pre-processing
step
CONCLUSION
In this paper, we present a new image fusion method
based on the CS theory and compare it with other methods.
Our method is done on blurred images. Visual analysis and
the quantitative evaluation show our fusion method performs
better than others. For blurred and noisy images, first salt and
pepper noise is removed from images ,then our fusion method
is applied on them. Finally, wiener filter minimized variation
the pixel value. In this case, also, we achieve better
performance with this new fusion method.
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PCA & CS based fusion for Medical Image Fusion

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