This research focuses of the efficacious wavelet-based methodology for clinical image fusion that is established by considering the human visual system, including the physical effects of the wavelet coefficients. Once the clinical images that have to be fused have been decomposed via the transforms of wavelet, different systems of fusion for integrating these coefficients are projected. The coefficients in the lower frequencies are chosen with the visibility-centered system, and those coefficients with the highest frequency bands are chosen using the variance-oriented approach. To effective mitigate the issue of noise and guarantee homogeneity of an image, which is being fused, coefficients are typically done based on the application of the window-centered verification process. The images are lastly structured using the inverse wavelet transforms with the composite coefficient. To effectively assess and effectively prove the effective applicability of the proposed methodology, experimentation series and comparison of the fusion approaches are done. The results of the experimentation on the real and simulated clinical images show that the projected approach is effective and is capable of yielding the proposed results of the fusion process.
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Cite this article
Arulmurugan Ramu and Anandakumar Haldorai, "Wavelet Methods and Pattern Recognition for Clinical Image Fusion", vol.1, no.1, pp. 050-057, January 2021. doi: 10.53759/0088/JBSHA202101007.
