access icon free HWFusion: Holoentropy and SP-Whale optimisation-based fusion model for magnetic resonance imaging multimodal image fusion

Image fusion is becoming a promising technique for obtaining a more informative image by combining various source images captured by multimodal imaging systems. The technique finds application in several fields, such as medical imaging, material analysis, satellite imaging, including defence and civilian sectors. This study presents a model, named holoentropy-whale fusion (HWFusion), for the image fusion. Two different multimodal images from magnetic resonance imaging (T1, T1C, T2, FLAIR) are fed into the wavelet transform to convert the images into four subbands. The wavelet coefficients are then fused using a weighted coefficient that utilises two factors, entropy and whale fusion factor, which are calculated using holoentropy and the proposed SP-Whale optimiser, respectively. SP-Whale is an algorithm designed by modifying whale optimisation algorithm with self-adaptive learning particle swarm optimisation and is used for the optimal selection of whale fusion factor. Inverse wavelet transform converts the fused wavelet coefficients obtained by the averaging of fusion factors into fused image. In a comparative analysis, the performance of HWFusion is compared with that of four existing techniques using, mutual information, peak signal-to-noise ratio, and root mean-squared error (RMSE), where it could attain mutual information of 1.8015, RMSE of 1.1701, and peak signal-to-noise ratio of 40.6575.

Inspec keywords: medical image processing; image fusion; optimisation; wavelet transforms; entropy; biomedical MRI

Other keywords: magnetic resonance imaging multimodal image fusion; SP-Whale optimisation-based fusion model; RMSE; multimodal imaging systems; T2; entropy factor; HWFusion; satellite imaging; mutual information; source images; wavelet transform; whale fusion factor; medical imaging; SP-Whale optimiser; defence sectors; weighted coefficient; wavelet coefficients; FLAIR; root mean-squared error; civilian sectors; holoentropy-whale optimisation-based fusion model; self-adaptive learning particle swarm optimisation; T1C; peak signal-to-noise ratio; material analysis; T1

Subjects: Medical magnetic resonance imaging and spectroscopy; Biology and medical computing; Function theory, analysis; Optimisation techniques; Optical, image and video signal processing; Integral transforms; Sensor fusion; Integral transforms; Computer vision and image processing techniques; Patient diagnostic methods and instrumentation; Biomedical magnetic resonance imaging and spectroscopy; Optimisation techniques

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