LAUSR

Pulse coupled neural network (PCNN) is widely used in image fusion framework due to its global coupling and pulse synchronization of neurons. However, its manual setting of parameters and inability to process multiple images affect the fusion performance. In this letter, a novel weighted parameter adaptive dual channel PCNN (WPADCPCNN) based medical fusion method is proposed in non-subsampled shearlet transform domain to fuse the magnetic resonance imaging and single-photon emission computed tomography images of AIDS dementia complex and Alzheimer's disease patients. The parameters of the proposed WPADCPCNN model are estimated from its inputs using fractal dimension. The high-pass sub-bands are fused using the WPADCPCNN model whereas the low-pass sub-bands are merged using a new weighted multi-scale morphological gradients based rule. Experimental results demonstrate that the proposed method outperforms some of the state-of-the-art methods in terms of both visual quality and objective assessment.