Medical image Processing gain much importance in today's era. Here we are posting Mediacal image Fusion in DICOM format.
IMAGE FUSION:
Image Fusion is a process of combining the relevant
information from a set of images of the same scene into a single image and the
resultant fused image will be more informative and complete than any of the
input images.
Input images could be multi sensor, multimodal, multi focus
or multi temporal. There are some important requirements for the image fusion
process:
- The fused image should preserve all relevant information
from the input images
- The image fusion should not introduce artifacts which can
lead to a wrong diagnosis
One of the important pre-processing steps for the fusion
process is image registration. Image registration is the process of
transforming different sets of data into one coordinate system. Image fusion
find application in the area of navigation guidance, object detection and
recognition, medical diagnosis, satellite imaging for remote sensing, military
and civilian surveillance, etc. Image fusion algorithms can be categorized into
different levels: pixel, feature, and decision levels. Pixel level fusion works
directly on the pixels of source images while feature level fusion algorithms
operate on features extracted from the source images.
PCA (Principal Component
Analysis) Based Image Fusion:
Principal Component Analysis is a sub space method, which
reduces the multidimensional data sets into lower dimensions for analysis. This
method determines the weights for each source. Image using the eigenvector
corresponding to the largest Eigen value of the covariance matrix of each
source image.
Discrete Wavelet Transform
Method:
Wavelet transforms
are multi-resolution image decomposition tool that provide a variety of
channels representing the image feature by different frequency sub bands at
multi-scale. It is a famous technique in analyzing signals. When decomposition
is performed, the approximation and detail component can be separated 2-D
Discrete Wavelet Transformation (DWT) converts the image from the spatial
domain to frequency domain. The image is divided by vertical and horizontal
lines and represents the first-order of DWT, and the image can be separated
with four parts those are LL1, LH1, HL1 and HH1.
Fig: GUI Before RUN
Fig: GUI After RUN
Youtube Video
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