Automatic identification of Mycobacterium tuberculosis by Gaussian mixture models

Journal of Microscopy

Volumn 223, Issue 2, 2006, Pages 120-132

  Forero, M G ^a   Cristobal G ^b   Desco, M ^c  

^a UNIVERSITY OF BIRMINGHAM   (United Kingdom)

^b INSTITUTO DE ÓPTICA   (Spain)

^c HOSPITAL GENERAL UNIVERSITARIO GREGORIO MARAÑÓN   (Spain)

Author keywords

Bayesian classification; Clustering; Colour; Feature extraction; Fluorescence microscopy; Gaussian mixture models; Tuberculosis

Indexed keywords

AUTOMATION; BACTERIOLOGY; DIAGNOSIS; DISEASE CONTROL; DISEASES; FLUORESCENCE MICROSCOPY; IMAGE ENHANCEMENT; IMAGE SEGMENTATION; PATTERN RECOGNITION;

AUTOMATIC IDENTIFICATION; BAYESIAN CLASSIFICATION; CLUSTERINGS; EARLY DIAGNOSIS; FEATURES EXTRACTION; GAUSSIAN MIXTURE MODEL; MYCOBACTERIOSIS; MYCOBACTERIUM TUBERCULOSIS; SAMPLE ANALYSIS; TUBERCULOSIS;

GAUSSIAN DISTRIBUTION;

ARTICLE; BACILLUS; BAYES THEOREM; GAUSSIAN MIXTURE MODEL; IMAGE ANALYSIS; IMAGE PROCESSING; LABORATORY AUTOMATION; MYCOBACTERIUM TUBERCULOSIS; NONHUMAN; PATTERN RECOGNITION; PRIORITY JOURNAL; PROCESS MODEL; STATISTICAL MODEL; TRAINING;

MYCOBACTERIUM TUBERCULOSIS;

EID: 33747148833     PISSN: 00222720     EISSN: 13652818     Source Type: Journal    
DOI: 10.1111/j.1365-2818.2006.01610.x     Document Type: Article

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