Paper Info
Title
Automatic Mouse Brain Extraction In Micro-Pet/Ct Images Based On A Modified Level-Set Method
Abstract
Micro-PET/CT has been widely used for brain imaging in diverse preclinical studies using mouse models. The precise brain extraction is an important pre-procedure to quantify the brain function based on micro-PET/CT images. In this study, we explored an automatic framework based on a modified level-set method (MLS) for mouse brain extraction in micro-PET/CT images. In the proposed MLS method, the initial level-set surface was automatically obtained by fuzzy C-means (FCM) clustering together with morphology processes. Then, the gradient vector flow (GVF) was used in the level-set evolution. Finally, the evolution iteration was optimized using average bandwidth energy (ABE) maximization. The results indicated that MLS method could achieve the accurate and robust brain extraction for experimental mouse data. Thus, the framework based on MLS has the potential in mouse brain volume delineation for the estimation of brain function in microPET/CT images.
Year
DOI
Venue
2015
10.1109/ICDSP.2015.7252047
2015 IEEE INTERNATIONAL CONFERENCE ON DIGITAL SIGNAL PROCESSING (DSP)
Keywords
Field
DocType
micro-PET/CT image, mouse, brain extraction, level-set, gradient vector flow
PET-CT,Computer vision,Pattern recognition,Computer science,Level set method,Fuzzy logic,Level set,Vector flow,Brain size,Artificial intelligence,Neuroimaging,Cluster analysis
Conference
Citations 
PageRank 
References 
0
0.34
1
Authors
3
Name
Order
Citations
PageRank
Xiujuan Zheng123.83
Shiye Chen200.34
Cheng Wang35811.05