Embedding topic discovery in conditional random fields model for segmenting nuclei using multispectral data

IEEE Transactions on Biomedical Engineering

Volumn 59, Issue 6, 2012, Pages 1539-1549

  Wu, Xuqing ^a   Amrikachi, Mojgan ^b   Shah, Shishir K ^a  

^a University of Houston   (United States)

^b HOUSTON METHODIST HOSPITAL   (United States)

Author keywords

Conditional random fields (CRFs); probabilistic latent semantic analysis (pLSA); segmentation; spectral microscopy; topic discovery

Indexed keywords

CELL SEGMENTATION; COLOR IMAGES; CONDITIONAL RANDOM FIELD; CONDITIONAL RANDOM FIELDS (CRFS); CONTEXTUAL INFORMATION; DATA SETS; HIGH-DIMENSIONAL DATASET; IMAGING MICROSCOPY; INTENSITY INHOMOGENEITY; LOW-LEVEL CUES; MULTI-CLASSIFICATION; MULTI-SPECTRAL DATA; PROBABILISTIC LATENT SEMANTIC ANALYSIS; ROBUST MODELS; SEGMENTATION ALGORITHMS; SEGMENTATION ERROR; SINGLE LAYER; SPECTRAL IMAGES; TOPDOWN; TOPIC DISCOVERY;

ALGORITHMS; CONTENT BASED RETRIEVAL; MULTILAYERS; RANDOM PROCESSES;

IMAGE SEGMENTATION;

ACCURACY; CELL COUNT; CELL NUCLEUS; CELL SEPARATION; CHROMATIN; CLASSIFICATION ALGORITHM; CONDITIONAL RANDOM FIELD; EMBEDDING; IMAGING SYSTEM; MATHEMATICAL ANALYSIS; REVIEW; SINGLE CELL ANALYSIS; SPECTROSCOPY;

ALGORITHMS; ANIMALS; ARTIFICIAL INTELLIGENCE; CELL NUCLEUS; COLORIMETRY; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MICROSCOPY; MODELS, BIOLOGICAL; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84861370255     PISSN: 00189294     EISSN: 15582531     Source Type: Journal    
DOI: 10.1109/TBME.2012.2188892     Document Type: Review

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