PET-CT image fusion using random forest and à-trous wavelet transform

International Journal for Numerical Methods in Biomedical Engineering
Ayan SealConsuelo Gonzalo-Martin

Abstract

New image fusion rules for multimodal medical images are proposed in this work. Image fusion rules are defined by random forest 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, random forest 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 is applied to reconstruct fused image. All experiments have been performed on 198 slices of both computed tomography and positron emission tomography 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 4 existing measures has been presented, which helps to compare the performance of 2 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.

References

Mar 11, 2009·IEEE Transactions on Information Technology in Biomedicine : a Publication of the IEEE Engineering in Medicine and Biology Society·Vince D Calhoun, Tülay Adali
Aug 13, 2001·Optics Express·Q GuihongY Pingfan
Dec 8, 2009·Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society·Nemir Al-AzzawiHaidi Ibrahim
Feb 2, 2013·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Shutao LiJianwen Hu
Jan 23, 2015·TheScientificWorldJournal·Xin-She YangTiew On Ting

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Citations

Jul 2, 2020·Applied Optics·Chinmaya PanigrahyEnrique Herrera-Viedma
Nov 6, 2020·Computers in Biology and Medicine·Samir JainAnis Yazidi

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