Instrumentation & Measurement Magazine 24-4 - 38

variable. Arithmetic calculation
by vector function of
each 2D-DDWT expression
of uniform Eigen vector
quantities is being done.
Inverse 2D-DDWT transformations
of the two
measured equations are being
calculated to obtain the
merged image.
Test Results
In this section, the experimental
results of the fusion
Fig. 2. Block diagram of proposed method.
parameters are done through a window-based accuracy testing
technique to resolve the occurrence of interference and
ensure diversity of the image to be fused. Fig. 2 represents the
details of the proposed method.
The merged image is essentially assembled by the inverse
2D-DDWT of all the reflective parameters. High frequency
sub-images typically reflect the precise elements of the source
images within the 2D-DDWT including edges, textures, and
area borders. Maximum absolute fusion law is commonly
used for a high frequency environment at this time. This methodology
lacks the association between adjacent pixels and is
sensitive to noise that can be easily mistaken for useful information
in the merged image. Consequently, a novel criterion
is introduced that allows maximum advantage of local image
knowledge and that can easily retrieve the image data
to make a smart decision on the collection of high frequency
parameters.
As the images are being decomposed using 2D-DDWT and
the parameters of 2D-DDWT are being acquired, Principle
Component Analysis is used as a fusion method to randomly
integrate the correct 2D-DDWT reference image parameters.
PCA transforms the original domain attributes into a new PCA
domain, in which they are classified according to their heterogeneity.
The fusion mechanism is accomplished in the PCA
domain by maintaining only those functions which comprise
considerable amounts of data. This could be attained by maintaining
only certain components that have a more variability.
Maximization of scaling rule is used for fusing the coefficients
obtained. The regression coefficient matrix is measured from
the coefficient at columns of 2D-DDWT parameters. Horizontal
vector covariance is processed. Root variables and matrix
regression coefficient values of the root are calculated. Column
variable referring to an extensive Eigen value is being obtained
by fractionating each element from the average of the Eigen
38
of MRI and CT images are
illustrated. Here five sets
of CT and MRI of human
brain tumor images were
experimented using the
proposed method. This has
also been experimented
with three existing methods,
namely method 1:
Laplacian Pyramid; method 2: Discrete Wavelet transform;
and method 3: Stationary wavelet transform. The merged images
of the projected method are being compared with method
1, method 2 and method 3. The input CT and MRI images of tumor
brain images are shown in Fig. 3. Set 1 images are (A1) &
(A2), Set 2 images are (B1) & (B2), Set 3 images are (C1) & (C2),
Set 4 images are (D1) & (D2), and Set 5 images are (E1) & (E2) of
CT and MRI of brain tumor, respectively.
Fig. 4, Fig. 5, and Fig. 6 show the fused images of Set 1, Set
2, Set 3, Set 4, and Set 5 for the existing Laplacian pyramid
method, the discrete wavelet transforms method and the stationary
wavelet method, respectively. Fig. 7 displays the output
fused image using the proposed 2D-DDWT PCA method. The
outlines and morphological features are definitely seen relative
to the original images in the proposed method. All edges and
textures are evidently seen in the proposed method merged
image. All information corresponding to bones and tissues
is visualized in the fused image which cannot be viewed in
Fig. 3. (A1), (B1), (C1), (D1), (E1)-CT images; (A2), (B2), (C2), (D2), (E2)-MRI
images.
IEEE Instrumentation & Measurement Magazine
June 2021

Instrumentation & Measurement Magazine 24-4

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