LAUSR

Abstract Medical image fusion has been used to improve useful and relevant information (e.g., precise localization of abnormalities) of multimodal medical images, such as computed tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET) images, and the information obtained from the fused images can then be utilized as an assistive tool for better diagnosis and treatment. In this work, we propose an algorithm for fusing multimodal medical images using lifting scheme-based biorthogonal wavelet transform. The multiscale fusion scheme is performed for the multimodal medical images in the wavelet domain at multiple scales by adopting either average or absolute maximum fusion rules. To verify the effectiveness of the proposed method, a series of visual and quantitative performance evaluations of the proposed method are compared with those of five representative wavelet-based fusion methods including contourlet transform (CLT), nonsubsampled CLT (NSCLT), lifting wavelet transform (LWT), multiwavelet transform (MWT), and stationary wavelet transform (SWT). For the quantitative performance evaluations, we adopted five metrics: fusion factor, fusion symmetry, entropy, standard deviation and edge strength. The experimental results demonstrated that the proposed method could yield better results than other wavelet transform-based fusion methods. Furthermore, from the additional comparison study of fusing noise-contaminated images, we could conclude that the proposed method is noise resilient in fusing images corrupted by Gaussian and speckle noise with varying variances.