Image Fusion Techniques and Quality Assessment Parameters for Clinical Diagnosis: A Review

Image fusion is a tool that serves to combine multi sensors images by using advanced image processing techniques. Particularly it serves best in medical diagnosis i.e. computed tomography (CT), magnetic resonance image (MRI), scan provides different types of information, by fusing them we can get accurate information for better clinical diagnosis. Transform domain image fusion methods such as wavelet transform, curvelet transform have its specific advantages while going for multi-sensors image fusion. Analysis is done to determine the image fusion algorithm which is more suitable for clinical diagnosis. Analysis is also done on image quality assessment parameters of image fusion. This paper presents a literature review on image fusion techniques and image quality assessment parameters such as Structural similarity index measure, laplacian mean squared error, mean squared error, Peak signal to noise ratio, entropy, structural content, Normalized cross correlation, Maximum difference, normalized absolute error. Comparison and effective use of all the techniques in image quality assessment is also