Hierarchical shape statistical model for segmentation of lung fields in chest radiographs

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

Volumn 5241 LNCS, Issue PART 1, 2008, Pages 417-424

  Shi, Yonghong ^a   Shen, Dinggang ^b  

^a FUDAN UNIVERSITY   (China)

^b UNIVERSITY OF NORTH CAROLINA SCHOOL OF MEDICINE   (United States)

Author keywords

Active shape model; Chest radiograph; Hierarchical shape statistics

Indexed keywords

ACTIVE SHAPE MODEL; AFFINITY PROPAGATION; ALIGNED SAMPLES; CHEST RADIOGRAPH; CHEST RADIOGRAPHS; HIERARCHICAL SHAPE STATISTICS; INITIAL SEGMENTATION; LUNG FIELDS; ORIGINAL SAMPLE; POPULATION MODEL; SEGMENTATION PERFORMANCE; SEGMENTATION PROCESS; SEGMENTATION RESULTS; SHAPE MODEL; SHAPE SIMILARITY; SHAPE STATISTICS; STATISTICAL MODELS; SUB-POPULATIONS; TESTING SAMPLES; TRAINING SAMPLE;

COMPUTER SCIENCE; DIGITAL IMAGE STORAGE; IMAGE SEGMENTATION; MEDICAL COMPUTING; MEDICAL IMAGING; OBJECT RECOGNITION; RADIOGRAPHY;

POPULATION STATISTICS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; IMAGE QUALITY; LUNG; METHODOLOGY; RADIOGRAPHY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL; THORAX RADIOGRAPHY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; LUNG; MODELS, BIOLOGICAL; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; RADIOGRAPHIC IMAGE ENHANCEMENT; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; RADIOGRAPHY, THORACIC; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 58849128362     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-85988-8_50     Document Type: Conference Paper

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