Medical images are available in different modalities, each with its own usage. For example, preferred use for CT scan is in imaging bone injuries, cardiothoracic imaging and cancer diagnosis. MRI is generally used for soft tissue imaging and brain tumour detection. Medical images from different modalities often yield pathological information and correspondingly physiological information as well. This has made the study of multimodal medical image fusion very attractive. There are many occasions which require the integration of such comprehensive information for clinical diagnosis. The system proposed contains a design for a multi-modality medical image fusion system using a Sugeno Fuzzy Logic (SFL) based fusion method. The efficiency of the SFL based fusion method is established by comparing it with the existing methods, such as Principal Component Analysis (PCA), Laplacian Pyramid Approach(LPA), Discrete Wavelet Transform (DWT), Redundancy Discrete Wavelet Transform (RDWT) and Dual-Tree Complex Wavelet Transform (DTCWT) using quantitative metrics such as Entropy (EN), Signal to Noise Ratio (SNR) and Mutual Information (MI). The experimental results reveal that SFL based fusion method provides better quality of information in terms of Entropy, shows less noise ratio in terms of SNR and the higher value of MI indicate that more information from the original images are transferred to the fused image. © 2016, International Journal of Pharmacy and Technology. All rights reserved.