Title:Medical Image Fusion Based on Local Saliency Energy and Multi-scale Fractal
Dimension
Volume: 20
Author(s): Yaoyong Zhou, Xiaoliang Zhu*, Panyun Zhou, Zhenwei Xu, Tianliang Liu, Wangjie Li and Renxian Ge
Affiliation:
- School of Software, Xinjiang University, Urumqi 830000, China
Keywords:
Image fusion, Local saliency energy, Multi-scale fractal dimension, Non-subsampled contourlet transform, Pulse coupled neural network, Phase consistency.
Abstract:
Background:
At present, there are some problems in multimodal medical image fusion, such as texture detail loss, leading to edge contour blurring and image
energy loss, leading to contrast reduction.
Objective:
To solve these problems and obtain higher-quality fusion images, this study proposes an image fusion method based on local saliency energy and
multi-scale fractal dimension.
Methods:
First, by using a non-subsampled contourlet transform, the medical image was divided into 4 layers of high-pass subbands and 1 layer of low-pass
subband. Second, in order to fuse the high-pass subbands of layers 2 to 4, the fusion rules based on a multi-scale morphological gradient and an
activity measure were used as external stimuli in pulse coupled neural network. Third, a fusion rule based on the improved multi-scale fractal
dimension and new local saliency energy was proposed, respectively, for the low-pass subband and the 1st closest to the low-pass subband. Layerhigh
pass sub-bands were fused. Lastly, the fused image was created by performing the inverse non-subsampled contourlet transform on the fused
sub-bands.
Results:
On three multimodal medical image datasets, the proposed method was compared with 7 other fusion methods using 5 common objective
evaluation metrics.
Conclusion:
Experiments showed that this method can protect the contrast and edge of fusion image well and has strong competitiveness in both subjective and
objective evaluation.
