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PET‐CT image fusion using random forest and à‐trous wavelet transform
Author(s) -
Seal Ayan,
Bhattacharjee Debotosh,
Nasipuri Mita,
RodríguezEsparragón Dionisio,
Menasalvas Ernestina,
GonzaloMartin Consuelo
Publication year - 2018
Publication title -
international journal for numerical methods in biomedical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.741
H-Index - 63
eISSN - 2040-7947
pISSN - 2040-7939
DOI - 10.1002/cnm.2933
Subject(s) - image fusion , artificial intelligence , wavelet transform , pixel , fusion , wavelet , computer vision , discrete wavelet transform , computer science , mathematics , fusion rules , pattern recognition (psychology) , image (mathematics) , algorithm , linguistics , philosophy
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.