A flexible and robust approach for segmenting cell nuclei from 2D microscopy images using supervised learning and template matching

Cytometry Part A

Volumn 83 A, Issue 5, 2013, Pages 495-507

  Chen, Cheng ^a   Wang, Wei ^a,b   Ozolek, John A ^c   Rohde, Gustavo K ^a,d  

^a CARNEGIE MELLON UNIVERSITY   (United States)

^b BEIJING JIAOTONG UNIVERSITY   (China)

^c CHILDREN S HOSPITAL OF PITTSBURGH   (United States)

^d CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

Cross correlation; Nonrigid registration; Nuclei; Segmentation; Template matching

Indexed keywords

ANIMAL; ARTICLE; AUTOMATED PATTERN RECOGNITION; BONE TUMOR; CELL LINE; CELL NUCLEUS; CELL STRAIN 3T3; COMPARATIVE STUDY; COMPUTER PROGRAM; EVALUATION; FEMALE; FIBROBLAST; HUMAN; IMAGE PROCESSING; METHODOLOGY; MICROSCOPY; MOUSE; OSTEOSARCOMA; PATHOLOGY; STATISTICAL MODEL; TUMOR CELL LINE;

ANIMALS; BONE NEOPLASMS; CELL LINE; CELL LINE, TUMOR; CELL NUCLEUS; FEMALE; FIBROBLASTS; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MICE; MICROSCOPY; MODELS, STATISTICAL; NIH 3T3 CELLS; OSTEOSARCOMA; PATTERN RECOGNITION, AUTOMATED; SOFTWARE;

EID: 84876790254     PISSN: 15524922     EISSN: 15524930     Source Type: Journal    
DOI: 10.1002/cyto.a.22280     Document Type: Article

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