PET-CT Image Fusion using Random Forest and À-trous Wavelet Transform

Summary

New image fusion rules for multimodal medical images are proposed in this work. Image fusion rules are defined by Random Forest (RF) learning algorithm and a translation-invariant à-trous wavelet transform (AWT). The proposed method is threefold. First, source images are decomposed into approximation and detail coefficients using AWT. Second, RF is used to choose pixels from the approximation and detail coefficients for forming the approximation and detail coefficients of the fused image. Lastly, inverse AWT (iAWT) is applied to reconstruct fused image. All experiments have been performed on 198 slices of both Computed Tomography (CT) and Positron Emission Tomography (PET) images of a patient. A traditional fusion method based on Mallat wavelet transform has also been implemented on these slices. A new image fusion performance measure along with four existing measures has been presented, which helps to compare the performance of two pixel level fusion methods. The experimental results clearly indicate that the proposed method outperforms the traditional method in terms of visual and quantitative qualities and the new measure is meaningful. This article is protected by copyright. All rights reserved.

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