Thyroid nodule recognition based on feature selection and pixel classification methods

Journal of Digital Imaging

Volumn 26, Issue 1, 2013, Pages 119-128

  Bibicu, Dorin ^a,b   Moraru, Luminita ^a   Biswas, Anjan ^c  

^a DUNAREA DE JOS UNIVERSITY OF GALATI   (Romania)

^b High School Dunarea   (Romania)

^c DELAWARE STATE UNIVERSITY   (United States)

Author keywords

CADi software; First order statistical features; Pixel classification; T test; Thyroid ultrasound images

Indexed keywords

AUTOMATIC EXTRACTION; CLASSIFICATION PERFORMANCE; CLASSIFICATION RATES; CLASSIFICATION RESULTS; FIRST-ORDER; MATHEMATICAL FOUNDATIONS; PIXEL CLASSIFICATION; RECEIVER OPERATING CHARACTERISTICS; REGION OF INTEREST; STATISTICAL APPROACH; STATISTICAL FEATURES; SUBCUTANEOUS TISSUES; T-TESTS; TEXTURE ANALYSIS METHOD; TEXTURE FEATURES; TEXTURE PRIMITIVES; TEXTURE VARIATION; THYROID NODULE; ULTRASOUND IMAGES;

SOFTWARE TESTING; TEXTURES; TISSUE; ULTRASONICS;

PIXELS;

ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; BIOPSY; COMPUTER ASSISTED DIAGNOSIS; ECHOGRAPHY; HUMAN; METHODOLOGY; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL; THYROID NODULE;

ALGORITHMS; BIOPSY; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; ROC CURVE; SENSITIVITY AND SPECIFICITY; THYROID NODULE;

EID: 84872230433     PISSN: 08971889     EISSN: 1618727X     Source Type: Journal    
DOI: 10.1007/s10278-012-9475-5     Document Type: Article

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