Abstract
According to the characteristics of a medical image, this paper presents a multimodality medical image fusion algorithm based on wavelet transform. For the low-frequency coefficients of the medical image, the fusion algorithm adopts the fusion rule of pixel absolute value maximization; for the high-frequency coefficients, the fusion algorithm uses the fusion rule that combines the regional information entropy contrast degree selection with the weighted averaging method. Then the fusion algorithm obtains the fused medical image with inverse wavelet transform. We select two groups of CT/MRI images and PET/ MRI images to simulate our fusion algorithm and compare its simulation results with the commonly-used wavelet transform fusion algorithm. The simulation results show that our fusion algorithm cannot only preserve more information on a source medical image but also greatly enhance the characteristic and brightness information of a fused medical image, thus being an effective and feasible medical image fusion algorithm.
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Teng, J., Wang, X., Zhang, J., Wang, S., Huo, P. (2010). A Multimodality Medical Image Fusion Algorithm Based on Wavelet Transform. In: Tan, Y., Shi, Y., Tan, K.C. (eds) Advances in Swarm Intelligence. ICSI 2010. Lecture Notes in Computer Science, vol 6146. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13498-2_82
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DOI: https://doi.org/10.1007/978-3-642-13498-2_82
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13497-5
Online ISBN: 978-3-642-13498-2
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