Abstract

The fusion of information from multiple imaging modalities plays a very important role in medical diagnosis. Wavelet-based transformations have been identified as powerful methods for this purpose due to their multiresolution nature, which enables the simultaneous preservation of both structural and fine-detail information across different frequency bands. In this paper we present experimental results obtained from the wavelet-based decomposition and fusion of medical images using Python and PyWavelets. Seven wavelets from four wavelet families—Daubechies (‘db1’, ‘db10’), biorthogonal (‘bior1.3’, ‘bior4.4’), coiflets (‘coif1’, ‘coif10’), and discrete Meyer (‘dmey’)—were systematically evaluated across three decomposition levels. An emphasis was put on the preservation of approximation and detail sub-images. Results outline that simpler wavelets used for the wavelet-based decomposition of grayscale medical images produce more details when compared with the colored medical images. From the tested fusion rules, and for the specific image pairs used in the analyses, we conclude that the average fusion rule gives the best information, without a lack of or excess of information regarding the visual quality of the fused image. Considering entropy as a quality metric and according to its higher values at all levels, the ‘bior4.4’ wavelet emerges as the best for wavelet-based image fusion. These findings could provide practical guidance for wavelet selection in multimodal medical image fusion pipelines.

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