Local binary patterns variants as texture descriptors for medical image analysis

Artificial Intelligence in Medicine

Volumn 49, Issue 2, 2010, Pages 117-125

  Nanni, Loris ^a   Lumini, Alessandra ^a   Brahnam, Sheryl ^b  

^a UNIVERSITY OF BOLOGNA   (Italy)

^b MISSOURI STATE UNIVERSITY   (United States)

Author keywords

Image medical analysis; Neonatal pain detection; Pap smear classification; Sub cellular protein localization; Support vector machine; Texture descriptors

Indexed keywords

MEDICAL ANALYSIS; NEONATAL PAIN; PAP-SMEAR CLASSIFICATION; SUB-CELLULAR; SUB-CELLULAR PROTEIN LOCALIZATION; TEXTURE DESCRIPTORS;

CLASSIFICATION (OF INFORMATION); CONTENT BASED RETRIEVAL; ENCODING (SYMBOLS); EXPERIMENTS; HEALTH; IMAGE ANALYSIS; LEARNING ALGORITHMS; PEDIATRICS; PROTEINS; SUPPORT VECTOR MACHINES; TEXTURES;

MEDICAL IMAGING;

ARTICLE; CALCULATION; CODING; CONTROLLED STUDY; DATA BASE; ELONGATED QUINARY PATTERN; EXPERIMENT; FACE; FLUORESCENCE MICROSCOPY; HELA CELL; IMAGE ANALYSIS; MACHINE LEARNING; MEASUREMENT; PAIN; PAPANICOLAOU TEST; PRIORITY JOURNAL; SUPPORT VECTOR MACHINE;

ARTIFICIAL INTELLIGENCE; DATA INTERPRETATION, STATISTICAL; DATABASES AS TOPIC; DIAGNOSTIC IMAGING; FACIAL EXPRESSION; FEMALE; HELA CELLS; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; INFANT, NEWBORN; MALE; MICROSCOPY, FLUORESCENCE; MODELS, STATISTICAL; PAIN; PAIN MEASUREMENT; PATTERN RECOGNITION, AUTOMATED; PHENOTYPE; PREDICTIVE VALUE OF TESTS; UTERINE CERVICAL NEOPLASMS; VAGINAL SMEARS;

EID: 77953363893     PISSN: 09333657     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.artmed.2010.02.006     Document Type: Article

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