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Multimodal medical image fusion plays a vital role in clinical diagnoses and treatment planning. In many image fusion methods-based pulse coupled neural network (PCNN), normalized coefficients are used to motivate the PCNN, and this makes the fused image blur, detail loss, and decreases contrast. Moreover, they are limited in dealing with medical images with different modalities. In this article, we present a new multimodal medical image fusion method based on discrete Tchebichef moments and pulse coupled neural network to overcome the aforementioned problems. First, medical images are divided into equal-size blocks and the Tchebichef moments are calculated to characterize image shape, and energy of blocks is computed as the sum of squared non-DC moment values. Then to retain edges and textures, the energy of Tchebichef moments for blocks is introduced to motivate the PCNN with adaptive linking strength. Finally, large firing times are selected as coefficients of the fused image. Experimental results show that the proposed scheme outperforms state-of-theart methods and it is more effective in processing medical images with different modalities.