LINKS: Learning-based multi-source IntegratioN frameworK for Segmentation of infant brain images

NeuroImage

Volumn 108, Issue , 2015, Pages 160-172

  Wang, Li ^a   Gao, Yaozong ^a,b   Shi, Feng ^a   Li, Gang ^a   Gilmore, John H ^a   Lin, Weili ^a   Shen, Dinggang ^a,c  

^a UNIVERSITY OF NORTH CAROLINA SCHOOL OF MEDICINE   (United States)

^b University of North Carolina   (United States)

^c Korea University   (South Korea)

Author keywords

Context feature; Infant brain images; Isointense stage; Multi modality; Random forest; Tissue segmentation

Indexed keywords

ARTICLE; CEREBROSPINAL FLUID; CONCEPTUAL FRAMEWORK; CONTROLLED STUDY; DIFFUSION TENSOR IMAGING; FRACTIONAL ANISOTROPY; FUNCTIONAL MAGNETIC RESONANCE IMAGING; FUNCTIONAL NEUROIMAGING; GRAY MATTER; HUMAN; IMAGE ANALYSIS; IMAGE PROCESSING; IMAGE QUALITY; INFANT; LEARNING BASED MULTI SOURCE INTEGRATION FRAMEWORK FOR SEGMENTATION OF INFANT BRAIN IMAGES; MYELINATION; PROBABILITY; WHITE MATTER; ALGORITHM; ANATOMY AND HISTOLOGY; AUTOMATED PATTERN RECOGNITION; BRAIN; BRAIN MAPPING; NEWBORN; NUCLEAR MAGNETIC RESONANCE IMAGING; PROCEDURES;

ALGORITHMS; BRAIN; BRAIN MAPPING; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; INFANT; INFANT, NEWBORN; MAGNETIC RESONANCE IMAGING; PATTERN RECOGNITION, AUTOMATED;

EID: 84920882082     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2014.12.042     Document Type: Article

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