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Title: A new pulse coupled neural network (PCNN) for brain medical image fusion empowered by shuffled frog leaping algorithm
Authors: Huang, Chenxi
Tian, Ganxun
Lan, Yisha
Peng, Yonghong
Hao, Yongtao
Cheng, Yongqiang
Che, Wenliang
Ng, Eddie Yin Kwee
Keywords: DRNTU::Engineering::Computer science and engineering
Single-Photon Emission Computed Tomography Image
Computed Tomography Image
Issue Date: 2019
Source: Huang, C., Tian, G., Lan, Y., Peng, Y., Ng, E. Y. K., Hao, Y., . . . Che, W. (2019). A new pulse coupled neural network (PCNN) for brain medical image fusion empowered by shuffled frog leaping algorithm. Frontiers in Neuroscience, 13, 210-. doi:10.3389/fnins.2019.00210
Series/Report no.: Frontiers in Neuroscience
Abstract: 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.
ISSN: 1662-4548
DOI: 10.3389/fnins.2019.00210
Rights: © 2019 Huang, Tian, Lan, Peng, Ng, Hao, Cheng and Che. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Fulltext Permission: open
Fulltext Availability: With Fulltext
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