Medical image fusion based on redundancy DWT and Mamdani type min-sum mean-of-max techniques with quantitative analysis

Image fusion is basically a process where multiple images (more than one) are combined to form a single resultant fused image. This fused image is more productive as compared to its original input images. The fusion technique in medical images is useful for resourceful disease diagnosis purpose. This paper illustrates different multimodality medical image fusion techniques and their results assessed with various quantitative metrics. Firstly two registered images CT (anatomical information) and MRI-T2 (functional information) are taken as input. Then the fusion techniques are applied onto the input images such as Mamdani type minimum-sum-mean of maximum (MIN-SUM-MOM) and Redundancy Discrete Wavelet Transform (RDWT) and the resultant fused image is analyzed with quantitative metrics namely Over all Cross Entropy(OCE), Peak Signal -to- Noise Ratio (PSNR), Signal to Noise Ratio (SNR), Structural Similarity Index(SSIM), Mutual Information(MI). From the derived results it is inferred that Mamdani type MIN-SUM-MOM is more productive than RDWT and also the proposed fusion techniques provide more information compared to the input images as justified by all the metrics.