Title
Robust automatic rodent brain extraction using 3-D pulse-coupled neural networks (PCNN).
Abstract
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.
Year
DOI
Venue
2011
10.1109/TIP.2011.2126587
IEEE Transactions on Image Processing
Keywords
Field
DocType
human brain imaging,brain extraction,large-scale rodent brain study,mouse brain,brain mri data,3-d pulse-coupled neural networks,robust automatic rodent brain,rodent brain,manual extraction,automatic method,semi-automated method,rodent brain extraction,three dimensional,solid modeling,indexing terms,image resolution,segmentation,neural nets,partial volume effect,mri,indexes,signal to noise ratio,indexation,level set,rodents
Computer vision,Pattern recognition,Computer science,Segmentation,Signal-to-noise ratio,Preprocessor,Human brain,Solid modeling,Artificial intelligence,Artificial neural network,Image resolution,Partial volume
Journal
Volume
Issue
ISSN
20
9
1941-0042
Citations 
PageRank 
References 
7
0.51
18
Authors
5
Name
Order
Citations
PageRank
Nigel Chou170.51
Jiarong Wu270.85
Jordan Bai Bingren370.51
Anqi Qiu457138.34
Kai-Hsiang Chuang516813.95