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231224s2011 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2011.2126587
|2 doi
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|a eng
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|a Chou, Nigel
|e verfasserin
|4 aut
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|a Robust automatic rodent brain extraction using 3-D pulse-coupled neural networks (PCNN)
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|c 2011
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 20.12.2011
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|a Date Revised 10.12.2019
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a Brain extraction is an important preprocessing step for further processing (e.g., registration and morphometric analysis) of brain MRI data. Due to the operator-dependent and time-consuming nature of manual extraction, automated or semi-automated methods are essential for large-scale studies. Automatic methods are widely available for human brain imaging, but they are not optimized for rodent brains and hence may not perform well. To date, little work has been done on rodent brain extraction. We present an extended pulse-coupled neural network algorithm that operates in 3-D on the entire image volume. We evaluated its performance under varying SNR and resolution and tested this method against the brain-surface extractor (BSE) and a level-set algorithm proposed for mouse brain. The results show that this method outperforms existing methods and is robust under low SNR and with partial volume effects at lower resolutions. Together with the advantage of minimal user intervention, this method will facilitate automatic processing of large-scale rodent brain studies
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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|a Wu, Jiarong
|e verfasserin
|4 aut
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|a Bai Bingren, Jordan
|e verfasserin
|4 aut
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|a Qiu, Anqi
|e verfasserin
|4 aut
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|a Chuang, Kai-Hsiang
|e verfasserin
|4 aut
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 20(2011), 9 vom: 15. Sept., Seite 2554-64
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|x 1941-0042
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|g year:2011
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|g day:15
|g month:09
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|u http://dx.doi.org/10.1109/TIP.2011.2126587
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