Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/104793
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dc.contributor.authorHuang, Chenxien
dc.contributor.authorTian, Ganxunen
dc.contributor.authorLan, Yishaen
dc.contributor.authorPeng, Yonghongen
dc.contributor.authorHao, Yongtaoen
dc.contributor.authorCheng, Yongqiangen
dc.contributor.authorChe, Wenliangen
dc.contributor.authorNg, Eddie Yin Kweeen
dc.date.accessioned2019-06-11T06:07:17Zen
dc.date.accessioned2019-12-06T21:39:53Z-
dc.date.available2019-06-11T06:07:17Zen
dc.date.available2019-12-06T21:39:53Z-
dc.date.issued2019en
dc.identifier.citationHuang, 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.2019.00210en
dc.identifier.issn1662-4548en
dc.identifier.urihttps://hdl.handle.net/10356/104793-
dc.description.abstractRecent 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.en
dc.format.extent10 p.en
dc.language.isoenen
dc.relation.ispartofseriesFrontiers in Neuroscienceen
dc.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.en
dc.subjectDRNTU::Engineering::Computer science and engineeringen
dc.subjectSingle-Photon Emission Computed Tomography Imageen
dc.subjectComputed Tomography Imageen
dc.titleA new pulse coupled neural network (PCNN) for brain medical image fusion empowered by shuffled frog leaping algorithmen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen
dc.identifier.doi10.3389/fnins.2019.00210en
dc.description.versionPublished versionen
item.grantfulltextopen-
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