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

• Published in: Knowledge-based systems Vol. 131; pp. 58 - 69
• Main Authors: Daniel, Ebenezer, Anitha, J., Gnanaraj, J.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.09.2017; Elsevier Science Ltd
• Subjects: Algorithms; Computed tomography; Computer vision; Cuckoo search algorithm; Decomposition; Discrete Wavelet Transform; Grey wolf optimization; Hybrid optimization; Image processing; Image quality; Masking; Medical image fusion; Medical imaging; Optimization; Performance indices; Performance measurement; Positron emission; Search algorithms; Tomography; Unsharp masking; Wavelet transforms; Medical image fusion; Wavelet transforms; Grey wolf optimization; Unsharp masking; Cuckoo search algorithm; Hybrid optimization
• ISSN: 0950-7051; 1872-7409
• DOI: 10.1016/j.knosys.2017.05.017

•The paper presents a novel optimum laplacian wavelet mask based fusion for getting anatomical and functional details.•Hybrid cuckoo search-grey wolf optimization for optimal selection of control parameters in grey wolf algorithm using cuckoo search algorithm.•Optimum multi scale selection using hybrid cuckoo search-grey wolf optimization.•The proposed fusion algorithm performed better results for MR-PET,MR-SPECT,MR-CT and MR-T1-T2 compared to existing techniques. 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.