Abstract

In the present study, functional and anatomical information is merged to analyze the prognosis of Alzheimer’s disease (AD). The shift-invariant non-subsampled shearlet transform (NSST) based decomposition technique is employed to restore all possible directional information of the source images. Then maximum root mean square of local absolute energy (RMLAE) based selection and normalized average gradient (NAG) scheme is applied for lower frequency subband (LFSB) and higher frequency subbands (HFSBs), respectively, to combine multimodal information in a single frame. The proposed fusion rule is able to combine the utmost information of LFSB and every finer texture related to edges and notches. The fused component has been generated by inverse NSST. Efficiency of the proposed fusion scheme has been reflected in the experimental results.KeywordsMRIMSTPET

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