Binary tissue classification on wound images with neural networks and bayesian classifiers

IEEE Transactions on Medical Imaging

Volumn 29, Issue 2, 2010, Pages 410-427

  Veredas, Francisco ^a   Mesa, Héctor ^b   Morente, Laura ^c  

^a UNIVERSITY OF MÁLAGA   (Spain)

^b Hospital University Carlos Haya de Málaga   (Spain)

^c Enfermería Comunitaría de Enlace   (Spain)

Author keywords

Artificial neural networks; Bayesian classifiers; Image segmentation; Mean shift smoothing; Tissue recognition; Wound evaluation

Indexed keywords

ARTIFICIAL NEURAL NETWORK; BAYESIAN CLASSIFIER; BAYESIAN CLASSIFIERS; MEAN SHIFT; MEAN SHIFT SMOOTHING;

CLASSIFIERS; COMPUTER VISION; DIGITAL IMAGE STORAGE; IMAGE SEGMENTATION; MULTILAYER NEURAL NETWORKS; PATTERN RECOGNITION SYSTEMS; TEXTURES; TISSUE; VECTOR QUANTIZATION; VISUAL COMMUNICATION;

BAYESIAN NETWORKS;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; BAYES THEOREM; COMPUTER ASSISTED DIAGNOSIS; DECUBITUS; HUMAN; IMAGE PROCESSING; METHODOLOGY; PATHOLOGY; PHOTOGRAPHY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; BAYES THEOREM; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGE PROCESSING, COMPUTER-ASSISTED; NEURAL NETWORKS (COMPUTER); PHOTOGRAPHY; PRESSURE ULCER; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 76249112941     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMI.2009.2033595     Document Type: Article

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