Intrinsic Image Decomposition-Based Grey and Pseudo-Color Medical Image Fusion

被引:21
|
作者
Du, Jiao [1 ]
Li, Weisheng [2 ]
Tan, Heliang [1 ]
机构
[1] Guangzhou Univ, Sch Comp Sci & Educ Software, Guangzhou 510006, Guangdong, Peoples R China
[2] Chongqing Univ Posts & Telecommun, Chongqing Key Lab Computat Intelligence, Chongqing 400065, Peoples R China
来源
IEEE ACCESS | 2019年 / 7卷
基金
中国国家自然科学基金;
关键词
Two-scale MRI-PET fusion; intrinsic image decomposition; structural and functional information; statistical significance test; ENHANCEMENT; BRAIN;
D O I
10.1109/ACCESS.2019.2900483
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:56443 / 56456
页数:14
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