Automatic cell detection in bright-field microscope images using sift, random forests, and hierarchical clustering

IEEE Transactions on Medical Imaging

Volumn 32, Issue 12, 2013, Pages 2274-2286

  Mualla, Firas ^a   Scholl, Simon ^a   Sommerfeldt, Bjorn ^a   Maier, Andreas ^a   Hornegger, Joachim ^a  

^a UNIVERSITY OF ERLANGEN NUREMBERG   (Germany)

Author keywords

Biomedical image processing; Image analysis; Machine learning; Microscopy; Object detection

Indexed keywords

BIO-MEDICAL IMAGE PROCESSING; DETECTION ERROR; HIER-ARCHICAL CLUSTERING; ILLUMINATION CONDITIONS; MICROSCOPE IMAGES; OBJECT DETECTION; PARAMETER-TUNING; SIMULATED IMAGES;

CELL CULTURE; DECISION TREES; IMAGE ANALYSIS; IMAGE PROCESSING; LEARNING SYSTEMS; MICROSCOPIC EXAMINATION;

CELLS;

ANALYTICAL ERROR; ANIMAL CELL; ARTICLE; AUTOANALYSIS; BRIGHT FIELD MICROSCOPY; CELL ADHESION; CELL COUNT; CELL LINE; CELL SIZE; CELL STRAIN L 929; CELL SUSPENSION; CHO CELL; CLUSTER ANALYSIS; FEMALE; ILLUMINATION; IMAGE ANALYSIS; IMAGE PROCESSING; MACHINE LEARNING; MICROSCOPE IMAGE; NONHUMAN; RANDOM FOREST; SIMULATION;

EID: 84890335131     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMI.2013.2280380     Document Type: Article

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