Feature level fusion of multimodal medical images in lifting wavelet transform domain.

A method for feature level image fusion for multimodal medical images in second generation wavelet domain (lifting wavelet transform domain) is proposed. The feature fused is edge and boundary information of input images that is extracted using wavelet transform modulus maxima criterion. The image fusion performance is evaluated by standard deviation, entropy, cross entropy and gradient parameters.