Learning structured models for segmentation of 2-D and 3-D imagery

IEEE Transactions on Medical Imaging

Volumn 34, Issue 5, 2015, Pages 1096-1110

  Lucchi, Aurelien ^a   Marquez Neila, Pablo ^b   Becker, Carlos ^b   Li, Yunpeng ^b   Smith, Kevin ^c   Knott, Graham ^b   Fua, Pascal ^b  

^a ETH ZURICH   (Switzerland)

^b EPFL   (Switzerland)

^c UNIVERSITY OF BASEL   (Switzerland)

Author keywords

Computer vision; electron microscopy; image processing; image segmentation; kernel methods; mitochondria; segmentation; statistical machine learning; structured prediction; superpixels; supervoxels

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER VISION; ELECTRON MICROSCOPES; ELECTRON MICROSCOPY; IMAGE PROCESSING; ITERATIVE METHODS; LEARNING SYSTEMS; MEDICAL IMAGING; MITOCHONDRIA; MOBILE SECURITY;

KERNEL METHODS; STATISTICAL MACHINE LEARNING; STRUCTURED PREDICTION; SUPERPIXELS; SUPERVOXELS;

IMAGE SEGMENTATION;

ARTICLE; DISCRIMINATION LEARNING; ELECTRON MICROSCOPY; IMAGE PROCESSING; KERNEL METHOD; MACHINE LEARNING; MAXIMUM LIKELIHOOD METHOD; NONLINEAR SYSTEM; RELIABILITY; SUPPORT VECTOR MACHINE; THREE DIMENSIONAL IMAGING; ALGORITHM; ANIMAL; CYTOLOGY; HIPPOCAMPAL CA1 REGION; MITOCHONDRION; PHYSIOLOGY; PROCEDURES; STATISTICAL MODEL; THEORETICAL MODEL;

ALGORITHMS; ANIMALS; CA1 REGION, HIPPOCAMPAL; IMAGING, THREE-DIMENSIONAL; MACHINE LEARNING; MICROSCOPY, ELECTRON; MITOCHONDRIA; MODELS, STATISTICAL; MODELS, THEORETICAL;

EID: 84929484680     PISSN: 02780062     EISSN: 1558254X     Source Type: Journal    
DOI: 10.1109/TMI.2014.2376274     Document Type: Article

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