An Integrated Dictionary-Learning Entropy-Based Medical Image Fusion Framework

Guanqiu Qi, Jinchuan Wang, Qiong Zhang, Fancheng Zeng and Zhiqin Zhu
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Guanqiu Qi: Collaborative Innovation Center for Industrial Internet of Things, College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Jinchuan Wang: Collaborative Innovation Center for Industrial Internet of Things, College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Qiong Zhang: Collaborative Innovation Center for Industrial Internet of Things, College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Fancheng Zeng: Collaborative Innovation Center for Industrial Internet of Things, College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Zhiqin Zhu: Collaborative Innovation Center for Industrial Internet of Things, College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Future Internet, 2017, vol. 9, issue 4, 1-25

Abstract: Image fusion is widely used in different areas and can integrate complementary and relevant information of source images captured by multiple sensors into a unitary synthetic image. Medical image fusion, as an important image fusion application, can extract the details of multiple images from different imaging modalities and combine them into an image that contains complete and non-redundant information for increasing the accuracy of medical diagnosis and assessment. The quality of the fused image directly affects medical diagnosis and assessment. However, existing solutions have some drawbacks in contrast, sharpness, brightness, blur and details. This paper proposes an integrated dictionary-learning and entropy-based medical image-fusion framework that consists of three steps. First, the input image information is decomposed into low-frequency and high-frequency components by using a Gaussian filter. Second, low-frequency components are fused by weighted average algorithm and high-frequency components are fused by the dictionary-learning based algorithm. In the dictionary-learning process of high-frequency components, an entropy-based algorithm is used for informative blocks selection. Third, the fused low-frequency and high-frequency components are combined to obtain the final fusion results. The results and analyses of comparative experiments demonstrate that the proposed medical image fusion framework has better performance than existing solutions.

Keywords: image fusion; sparse representation; dictionary learning; medical image (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2017
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