Toward precise pulmonary nodule descriptors for nodule type classification

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6363 LNCS, Issue PART 3, 2010, Pages 626-633

  Farag, Amal ^a   Elhabian, Shireen ^a   Graham, James ^a   Farag, Aly ^a   Falk, Robert ^b  

^a University of Louisville   (United States)

^b JEWISH HOSPITAL   (United States)

Author keywords

Gabor wavelet; nodule type classification; SIFT; texture descriptors

Indexed keywords

DESCRIPTORS; DIMENSIONAL SUBSPACE; FEATURE-BASED; GABOR WAVELETS; IRIS CODE; IRIS RECOGNITION ALGORITHM; LOW-DOSE CT; LUNG NODULE; NODULE TYPE CLASSIFICATION; PULMONARY NODULES; SCALE INVARIANT FEATURE TRANSFORMS; SIFT; TEXTURE DESCRIPTORS;

ALGORITHMS; BIOMETRICS; COMPUTERIZED TOMOGRAPHY; MEDICAL COMPUTING; MEDICAL IMAGING; TEXTURES;

FEATURE EXTRACTION;

ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; COMPUTER ASSISTED TOMOGRAPHY; HUMAN; IMAGE QUALITY; LUNG; LUNG NODULE; LUNG TUMOR; METHODOLOGY; RADIOGRAPHY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; HUMANS; LUNG; LUNG NEOPLASMS; PATTERN RECOGNITION, AUTOMATED; RADIOGRAPHIC IMAGE ENHANCEMENT; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SOLITARY PULMONARY NODULE; TOMOGRAPHY, X-RAY COMPUTED;

EID: 84876194732     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15711-0_78     Document Type: Conference Paper

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