Globally optimal tumor segmentation in PET-CT images: A graph-based co-segmentation method

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

Volumn 6801 LNCS, Issue , 2011, Pages 245-256

  Han, Dongfeng ^a   Bayouth, John ^a   Song, Qi ^b   Taurani, Aakant ^b   Sonka, Milan ^a   Buatti, John ^a   Wu, Xiaodong ^a,b  

^a UNIVERSITY OF IOWA   (United States)

^b IA United States   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLINICAL PRACTICES; CT IMAGE; GRAPH CUT; GRAPH-BASED; HAUSDORFF DISTANCE; IMAGE PAIRS; IMAGING MODALITY; LOW CONTRAST; LOW-ORDER POLYNOMIALS; MARKOV RANDOM FIELD; MAXIMUM FLOWS; OPTIMAL SOLUTIONS; SEGMENTATION ACCURACY; SIMILARITY COEFFICIENTS; SPATIAL RESOLUTION; TUMOR SEGMENTATION;

COMPUTERIZED TOMOGRAPHY; DATA PROCESSING; IMAGE RESOLUTION; MEDICAL IMAGING; OPTIMIZATION; POLYNOMIAL APPROXIMATION; STRUCTURAL FRAMES; TUMORS;

IMAGE SEGMENTATION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; COMPUTER ASSISTED TOMOGRAPHY; HEAD AND NECK TUMOR; HUMAN; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; METHODOLOGY; POSITRON EMISSION TOMOGRAPHY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; HEAD AND NECK NEOPLASMS; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; PATTERN RECOGNITION, AUTOMATED; POSITRON-EMISSION TOMOGRAPHY; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE; TOMOGRAPHY, X-RAY COMPUTED;

EID: 79959599387     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-22092-0_21     Document Type: Conference Paper

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