@article{MAKHILLIJSC202015221479,
    title = {Medical Image Fusion of Multi Modal Images using Random Block Selection Method},
    journal = {International Journal of Soft Computing},
    volume = {15},
    number = {2},
    pages = {30-33},
    year = {2020},
    issn = {1816-9503},
    doi = {ijscomp.2020.30.33},
    url = {https://makhillpublications.co/view-article.php?issn=1816-9503&doi=ijscomp.2020.30.33},
    author = {D. Sheefa and},
    keywords = {CT,PET,image fusion,contourlet,information},
    abstract = {Image fusion is a process of obtaining a single
image by combining multiple images. Since, the source
images are obtained from various detectors at dissimilar
times, some information will be missing in one source
image and that missing information may present in
another source image. Hence, the physicians are in the
need of combining the fine information from both source
images into a single one. The objective of image fusion is
providing utmost information that are missing in the
source images. In this study CT (Computed Tomography)
and PET (Positron Emission Tomography) images are get
fused. The CT imaging provides anatomic information
whereas the PET image provides functional information
of the body. The proposed method fused the images using
contourlet based random block selection method with
MAX fusion rule. It is proved that the proposed method
provides better result with less number of computations.
Experimental results are taken by writing MATLAB code.
To prove the quality of fused image, performance
measures such as Peak Signal to Noise Ratio (PSNR),
Structural Similarity Index Measure (SSIM) and Mutual
Information (MI) are used.}
    }