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A New Pulse Coupled Neural Network (PCNN) for Brain Medical Image Fusion Empowered by Shuffled Frog Leaping Algorithm.

Huang, Chenxi, Tian, Ganxun, Lan, Yisha, Peng, Yonghong, Ng, E Y K, Hao, Yongtao, Cheng, Yongqiang and Che, Wenliang (2019) A New Pulse Coupled Neural Network (PCNN) for Brain Medical Image Fusion Empowered by Shuffled Frog Leaping Algorithm. Frontiers in neuroscience, 13. p. 210. ISSN 1662-453X

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Recent research has reported the application of image fusion technologies in medical images in a wide range of aspects, such as in the diagnosis of brain diseases, the detection of glioma and the diagnosis of Alzheimer's disease. In our study, a new fusion method based on the combination of the shuffled frog leaping algorithm (SFLA) and the pulse coupled neural network (PCNN) is proposed for the fusion of SPECT and CT images to improve the quality of fused brain images. First, the intensity-hue-saturation (IHS) of a SPECT and CT image are decomposed using a non-subsampled contourlet transform (NSCT) independently, where both low-frequency and high-frequency images, using NSCT, are obtained. We then used the combined SFLA and PCNN to fuse the high-frequency sub-band images and low-frequency images. The SFLA is considered to optimize the PCNN network parameters. Finally, the fused image was produced from the reversed NSCT and reversed IHS transforms. We evaluated our algorithms against standard deviation (SD), mean gradient (Ḡ), spatial frequency (SF) and information entropy (E) using three different sets of brain images. The experimental results demonstrated the superior performance of the proposed fusion method to enhance both precision and spatial resolution significantly.

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Additional Information: ** From Europe PMC via Jisc Publications Router ** History: ppub 01-01-2019; epub 20-03-2019. ** Licence for this article: cc by
Uncontrolled Keywords: Image fusion, Pulse Coupled Neural Network, Computed Tomography Image, Shuffled Frog Leaping, Single-photon Emission Computed Tomography Image
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Item ID: 10727
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ISSN: 1662-453X
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Date Deposited: 10 Mar 2020 11:10
Last Modified: 10 Mar 2020 11:11


Author: Chenxi Huang
Author: Ganxun Tian
Author: Yisha Lan
Author: Yonghong Peng
Author: E Y K Ng
Author: Yongtao Hao
Author: Yongqiang Cheng
Author: Wenliang Che

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Faculty of Technology > School of Computer Science

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