Paper Info
Title
Intrinsic image decomposition-based grey and pseudo-color medical image fusion
Abstract
It is difficult to extract both structural and functional information from the input grey magnetic resonance imaging (MRI) and pseudo-color positron emission tomography (PET) images using the same decomposition scheme in multi-scale transform fusion methods. To overcome this limitation, we propose two algorithms based on intrinsic image decomposition to decompose MRI and PET images into its two separate components in the spatial domain. Algorithm 1 could extract structural information while reducing the noise from the MRI image. Algorithm 2 is for averaging the color information from the PET image. As for the image fusion rule, the defined importance of image coefficients is used to combine the decomposed two-scale components to produce the final fused image, which could keep more spatial resolution with substitution strategies. It demonstrates that the proposed fusion methods could improve the values of mutual information by the metrics on the disease database. Furthermore, the proposed methods produce the competitive visual signal-to-noise ratio values on experiments for robustness database. In addition to the variance in metrics values, the non-parametric Friedman test and the post-hoc Bonferroni-Dunn test are used to analyze the significant difference between the proposed and the state-of-the-arts methods.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2900483
IEEE ACCESS
Keywords
Field
DocType
Two-scale MRI-PET fusion,intrinsic image decomposition,structural and functional information,statistical significance test
Computer vision,Image fusion,Computer science,Artificial intelligence,Instrumental and intrinsic value,Distributed computing
Journal
Volume
ISSN
Citations 
7
2169-3536
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Jiao Du16810.21
Weisheng Li214129.73
Heliang Tan300.34