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. Experimental results show that the proposed method gives better results for image fusion as image contrast, average information content and detail information of fused image are increased. This method has further advantages of fast implementation, flexibility, saving of auxiliary memory, property of perfect reconstruction and simplicity as we have used lifting wavelet transform. The reduction in computational complexity has been achieved by a factor of two as compared to the nonlifted wavelet transform.