Abstract
Image compression or fusion is the concept of identifying in-depth parameters of disease variables, and requires output images that preserve all the viable and prominent information that is gathered from source images without any further introduction of artifacts or unnecessary distortions. Measurement of images for prospective evaluation and image fusion depends on various performance measures, such as structure similarity index, standard deviation, edge detection, correlation coefficient and high pass correlation, average gradient, root-mean-square error, peak signal-to-noise ratio, entropy, etc. This review discusses various medical image fusion modalities focused on Principal Component Analysis, Independent Component Analysis, and wavelet transform. An introduction to the usefulness of such modalities is presented, suggesting safe hybrid modality combinations that could greatly enhance the image fusion process. Novel trends in medical image fusion techniques to achieve a perfectly desired, quality image, the future prospects of an ideal technique for medical imaging, and recognition of diseases are covered.
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