A novel 3D joint MGRF framework for precise lung segmentation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7588 LNCS, Issue , 2012, Pages 86-93

  Abdollahi, Behnoush ^a   Soliman, Ahmed ^a   Civelek, A C ^b   Li, X F ^b   Gimel'farb, G ^c   El Baz, Ayman ^a  

^a University of Louisville   (United States)

^b University of Louisville School of Medicine   (United States)

^c UNIVERSITY OF AUCKLAND   (New Zealand)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN FUSION; COMPUTED TOMOGRAPHY; CT IMAGE; DATA GENERATION; EXECUTION TIME; GAUSSIAN KERNELS; GAUSSIAN SCALE SPACE; GAUSSIANS; IMAGE SIGNAL; INITIAL SEGMENTATION; LINEAR COMBINATIONS; LUNG REGIONS; LUNG SEGMENTATION; LUNG TISSUE; MARKOV-GIBBS RANDOM FIELDS; NVIDIA GRAPHICS; REAL DATA SETS; SIMILARITY COEFFICIENTS; SINGLE-THREADED;

BIOLOGICAL ORGANS; COMPUTER GRAPHICS; GAUSSIAN DISTRIBUTION; HISTOLOGY; ITERATIVE METHODS; LEARNING SYSTEMS; MEDICAL IMAGING; PROGRAM PROCESSORS; TISSUE;

COMPUTERIZED TOMOGRAPHY;

EID: 84870045425     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-35428-1_11     Document Type: Conference Paper

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