  In recent years, many research achievements are made in the medical image
fusion field. Medical Image fusion means that several of various modality image
information is comprehended together to form one image to express its
information. The aim of image fusion is to integrate complementary and
redundant information. CT/MRI is one of the most common medical image fusion.
These medical modalities give information about different diseases.
Complementary information is offered by CT and MRI. CT provides the best
information about denser tissue and MRI offers better information on soft
tissue. There are two approaches to image fusion, namely Spatial Fusion and
Transform fusion. Transform fusion uses transform for representing the source
images at multi-scale. This paper presents a Wavelet Transform image fusion
methodology based on the intensity magnitudes of the wavelet coefficients and
compares five variations of the wavelet transform implemented separately in
this fusion model. The image fusion model, using the Discrete Wavelet Transform
(DWT), the Stationary Wavelet Transform (SWT), the Integer Lifting Wavelet
Transform (ILFT) the dual-tree Complex Wavelet Transform (DT CWT) and dual-tree
Q-shift dual-tree CWT, is applied to multi-modal images. The resulting fused
images are compared visually and through benchmarks such as Entropy (E), Peak
Signal to Noise Ratio, (PSNR), Root Mean Square Error (RMSE), Image Quality
Index (IQI) and Standard deviation (SD) computations.
