Instrumentation & Measurement Magazine 24-4 - 35

Efficient Medical Image Fusion
Using 2-Dimensional Double
Density Wavelet Transform to
Improve Quality Metrics
V. Amala Rani and S. Lalithakumari
I
n the recent past, a medical 2D image fusion approach
has played a vital role in the biomedical industry for diagnosing
disease using various modalities of medical
images. Biomedical 2D image fusion synthesis is a combining
technique that merges useful information from two or more
modalities of data captured through high definition scanning
devices. The fused image is highly useful when doctors make
emergency surgical decisions for their clinical patients. In this
research, an innovative medical 2D image fusion approach
called 2-Dimensional Double Density Wavelet Transform
(2D-DDWT), based on maximization of scaling rule, is being
proposed for the combination of CT and MRI images of human
brain. The source image is applied to 2D-DDWT pursued by
the fusion of sub images of coefficients based on high and low
frequencies that are decomposed by transformation technique.
The low frequency information is decomposed using the congruency
rule of phase while the high frequency information is
decomposed using 2D Gabor filter. The 2D-DDWT frequency
coefficients are fused by using Principle Component Analysis
(PCA) for the approximation parameters, and therefore the
rule of selection maximum is being applied for the coefficients
to improve features of image fusion such as segmentation.
Maximization scaling rule is proposed for pixel level fusion
to improve quality metrics. The quantitative and qualitative
analysis proves the efficiency of the proposed methodology
and demonstrates the improvement of the proposed methodology
over existing methods.
Overview of Medical Image Fusion
Nowadays, many people all over the world are affected by
brain tumors. The collection of unusual cells in the brain causes
the tumor. This could be a malignant cancerous tumor type or
a benign non-cancerous tumor type. The tissues are analyzed
by Computed tomography (CT) and Magnetic Resonance Imaging
(MRI) images. Multi-modality clinical 2D image fusion
is a combination of several procedures of multimodal images.
The aim is to get a better clinically effective image while
the critical knowledge obtained from either of the images collected
using inertial sensors is improved. Medical imaging
June 2021
is now an important part of routine clinical settings. Medical
image fusion provides an essential solution that produces
a high-quality image through incorporating complementary
functionality of various imaging techniques. Following
tremendous advancements, no one paradigm currently remains
that may reflect all aspects of the human body. Fused
images provide further useful scientific information for effective
evaluation, clinical preparation, and multiple illnesses
in follow-up trials. The CT is suitable for analyzing complex
structures while MRI provides high resolution of soft tissues.
CT scans provide outstanding statistics on bony tissue, while
MRIs provide information on fragile or mucosal tissue. A requirement
for MRI and CT image fusion lies in the assumption
that it can only be performed on different samples if the images
require any performance changes, while fusion will save different
image processing efforts. The benefit of this is to reduce
the storage space for specific scans. A proper description of the
tumors and their borders is critical for diagnoses. All the information
could be preserved in a single image which could pave
the way for earlier diagnosis of the disease by the clinicians.
There have been numerous techniques implemented for image
fusion. Three groups of fusion are being categorized, namely
pixel-based fusion, feature-based fusion and decision-level fusion.
The pixel level fusion is able to preserve all the spatial
information in an image [1]. Hence, it is being considered better
than other methods of fusion [2].
In this paper, a novel multimodal image fusion is being
proposed that utilizes a 2-Dimensional Double Density Wavelet
Transform and pixel-based fusion for CT and MRI of tumor
images. The paper discusses the related works done, introduces
2D-DWT, explains Principal Component Analysis,
presents the proposed fusion method, and gives the experimental
results and performance metrics before its conclusion.
Shweta Goel et al. [3] has proposed the multi-modal clinical
2D images, combined using the frequency decomposition
method Dual Tree Discrete Wavelet Transform and fused using
Principal Component Analysis (PCA) for optimum specification
law for exact parameters of contrast enhancement. T.
Akbarpour et al. [4] depicted a novel technique to merge MR
IEEE Instrumentation & Measurement Magazine
1094-6969/21/$25.00©2021IEEE
35

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