Robust selection-based sparse shape model for lung cancer image segmentation

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

Volumn 8151 LNCS, Issue PART 3, 2013, Pages 404-412

  Xing, Fuyong ^a,b   Yang, Lin ^a,b  

^a UNIVERSITY OF KENTUCKY   (United States)

^b UNIVERSITY OF KENTUCKY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BACKGROUND CLUTTER; BETTER PERFORMANCE; CELLULAR LEVELS; DEFORMABLE MODELING; IMAGE-GUIDED DIAGNOSIS; MORPHOLOGICAL FEATURES; SEGMENTATION ALGORITHMS; STATE-OF-THE-ART METHODS;

ALGORITHMS; BIOLOGICAL ORGANS; DIAGNOSIS; DISEASES; MEDICAL COMPUTING; MEDICAL IMAGING;

IMAGE SEGMENTATION;

ALGORITHM; ARTICLE; AUDIOVISUAL EQUIPMENT; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; CELL POLARITY; CELL SIZE; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; HUMAN; IMAGE ENHANCEMENT; LUNG NON SMALL CELL CANCER; LUNG TUMOR; METHODOLOGY; MICROSCOPY; PATHOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; CARCINOMA, NON-SMALL-CELL LUNG; CELL POLARITY; CELL SIZE; COMPUTER SIMULATION; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; LUNG NEOPLASMS; MICROSCOPY; MODELS, ANATOMIC; MODELS, BIOLOGICAL; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84894628216     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-40760-4_51     Document Type: Conference Paper

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