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Title: PET-CT image fusion using random forest and à-trous wavelet transform
Authors: Seal, Ayan
Bhattacharjee, Debotosh
Nasipuri, Mita
Rodríguez-Esparragón, Dionisio 
Menasalvas, Ernestina
Gonzalo-Martin, Consuelo
UNESCO Clasification: 1203 Ciencia de los ordenadores
3325 Tecnología de las telecomunicaciones
2209 Óptica
120325 Diseño de sistemas sensores
220990 Tratamiento digital. Imágenes
Keywords: Computed tomography images
Fusion metrics
Fusion rules
Medical image fusion
Positron emission tomography, et al
Issue Date: 2017
Journal: International Journal for Numerical Methods in Biomedical Engineering 
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.
ISSN: 2040-7939
DOI: 10.1002/cnm.2933
Source: International Journal for Numerical Methods in Biomedical Engineering [ISSN 2040-7939], v. 34 (3), e2933
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