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In this paper, a novel multimodality Medical Image Fusion (MIF) method, based on Ripplet Transform Type-I (RT) using Pulse-Coupled Neural Network (PCNN) is presented. The proposed MIF scheme exploits the advantages of both RT and PCNN to obtain better results. The source medical images are first decomposed by discrete RT (DRT). The low-frequency subbands (LFSs) are fused using the `max selection' rule. For the fusion of high-frequency subbands (HFSs) a PCNN model is utilized. Modified Spatial Frequency (MSF) in DRT domain is input to motivate the PCNN and coefficients in DRT domain with large firing times are selected as coefficients of the fused image. Then inverse DRT (IDRT) is applied to the fused coefficients to get the fused image. The performance of the proposed scheme is evaluated by various quantitative measures like Mutual Information (MI), Spatial Frequency (SF), and Entropy (EN) etc. Visual and quantitative analysis and comparisons show the effectiveness of the proposed scheme in fusing multimodality medical images.