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Entropy 2019, 21(3), 267; https://doi.org/10.3390/e21030267

A New Dictionary Construction Based Multimodal Medical Image Fusion Framework

1
School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100083, China
2
School of Physics and Optoelectronic Engineering, Foshan University, Foshan 528000, China
*
Author to whom correspondence should be addressed.
Received: 12 January 2019 / Revised: 22 February 2019 / Accepted: 4 March 2019 / Published: 9 March 2019
(This article belongs to the Special Issue Information Theory Applications in Signal Processing)
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Abstract

Training a good dictionary is the key to a successful image fusion method of sparse representation based models. In this paper, we propose a novel dictionary learning scheme for medical image fusion. First, we reinforce the weak information of images by extracting and adding their multi-layer details to generate the informative patches. Meanwhile, we introduce a simple and effective multi-scale sampling to implement a multi-scale representation of patches while reducing the computational cost. Second, we design a neighborhood energy metric and a multi-scale spatial frequency metric for clustering the image patches with a similar brightness and detail information into each respective patch group. Then, we train the energy sub-dictionary and detail sub-dictionary, respectively by K-SVD. Finally, we combine the sub-dictionaries to construct a final, complete, compact and informative dictionary. As a main contribution, the proposed online dictionary learning can not only obtain an informative as well as compact dictionary, but can also address the defects, such as superfluous patch issues and low computation efficiency, in traditional dictionary learning algorithms. The experimental results show that our algorithm is superior to some state-of-the-art dictionary learning based techniques in both subjective visual effects and objective evaluation criteria. View Full-Text
Keywords: medical image fusion; dictionary learning; sparse representation; multi-scale spatial frequency medical image fusion; dictionary learning; sparse representation; multi-scale spatial frequency
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Zhou, F.; Li, X.; Zhou, M.; Chen, Y.; Tan, H. A New Dictionary Construction Based Multimodal Medical Image Fusion Framework. Entropy 2019, 21, 267.

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