Ultrasound-based characterization of prostate cancer: An in vivo clinical feasibility study

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

Volumn 8150 LNCS, Issue PART 2, 2013, Pages 279-286

  Imani, Farhad ^a   Abolmaesumi, Purang ^b   Gibson, Eli ^c   Galesh Khale, Amir Khojaste ^a   Gaed, Mena ^c   Moussa, Madeleine ^d   Gomez, Jose A ^d   Romagnoli, Cesare ^c   Siemens, D Robert ^e   Leviridge, Michael ^e   Chang, Silvia ^f   Fenster, Aaron ^c   Ward, Aaron D ^c   Mousavi, Parvin ^a  

^a QUEEN S UNIVERSITY   (Canada)

^b UNIVERSITY OF BRITISH COLUMBIA   (Canada)

^c UNIVERSITY OF WESTERN ONTARIO   (Canada)

^d LONDON HEALTH SCIENCES CENTRE   (Canada)

^e KINGSTON GENERAL HOSPITAL   (Canada)

^f VANCOUVER GENERAL HOSPITAL   (Canada)

Author keywords

prostate cancer; RF time series; tissue characterization

Indexed keywords

AREA UNDER ROC CURVE (AUC); CLASSIFICATION ACCURACY; CLINICAL FEASIBILITY; CROSS VALIDATION; MEAN CENTRAL FREQUENCIES; PROSTATE CANCERS; TIME SERIES FEATURES; TISSUE CHARACTERIZATION;

CHARACTERIZATION; TIME SERIES; TISSUE; ULTRASONICS;

DISEASES;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; ECHOGRAPHY; FEASIBILITY STUDY; HUMAN; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; MALE; METHODOLOGY; PROSTATE TUMOR; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; FEASIBILITY STUDIES; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MALE; PATTERN RECOGNITION, AUTOMATED; PROSTATIC NEOPLASMS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE; ULTRASONOGRAPHY;

EID: 84897572791     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-40763-5_35     Document Type: Conference Paper

References (11)

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