Optimum laplacian wavelet mask based medical image using hybrid cuckoo search - grey wolf optimization algorithm

Multi scale masking techniques are well known in the field of multi modal medical image fusion. In medical image fusion the quality of complement information is important .In multi modal medical image fusion discrete wavelet transform (db4) based techniques provides greater level of approximation but the edge features available is less. The laplacian filter based techniques provides grater edge features. In this paper we propose an Optimum Laplacian Wavelet Mask (OLWM) based fusion using Hybrid Cuckoo Search -Grey Wolf Optimization (HCS-GWO) for multi modal medical image fusion. The HCS-GWO can automatically select the control parameters of grey wolf algorithm using cuckoo search parameters. First, the proposed fusion approach is validated for MR-SPECT, MR-PET, MR-CT and MR T1-T2 image fusion using various fusion evaluation indexes. Later, the conventional grey wolf optimization is modified with cuckoo search algorithm. Experimental results are analyzed using various performance metrics and our OLWM shows improved results than other conventional decomposition techniques.