High-throughput detection of prostate cancer in histological sections using probabilistic pairwise Markov models

Medical Image Analysis

Volumn 14, Issue 4, 2010, Pages 617-629

  Monaco, James P ^a   Tomaszewski, John E ^b   Feldman, Michael D ^b   Hagemann, Ian ^b   Moradi, Mehdi ^c   Mousavi, Parvin ^c   Boag, Alexander ^c   Davidson, Chris ^c   Abolmaesumi, Purang ^c   Madabhushi, Anant ^a  

^a RUTGERS UNIVERSITY   (United States)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

^c QUEEN S UNIVERSITY   (Canada)

Author keywords

Digital pathology; Histology; Markov random fields; Prostate cancer detection

Indexed keywords

AUTOMATED GLEASON GRADING; DESKTOP COMPUTER; DETECTION SENSITIVITY; DETECTION SYSTEM; DIAGNOSTIC RESOLUTION; DIGITAL PATHOLOGY; FALSE POSITIVE RATES; HIGH-THROUGHPUT; HIGH-THROUGHPUT DETECTION; HISTOLOGICAL SECTION; LOW RESOLUTION; MARKOV MODEL; MARKOV RANDOM FIELD; MARKOV RANDOM FIELDS; MATHEMATICAL ABSTRACTION; POTENTIAL FUNCTION; PROSTATE CANCER DETECTION; PROSTATE CANCERS; RADICAL PROSTATECTOMY;

ALGORITHMS; DISEASES; HISTOLOGY; IMAGE SEGMENTATION; MARKOV PROCESSES; PATHOLOGY; PIXELS; POLYMETHYL METHACRYLATES; THROUGHPUT;

PROBABILITY DENSITY FUNCTION;

ALGORITHM; ARTICLE; CANCER DIAGNOSIS; COMPUTER ASSISTED DIAGNOSIS; CONTROLLED STUDY; DENSITY; DIAGNOSTIC ACCURACY; DIAGNOSTIC VALUE; DIGITAL IMAGING; GLEASON SCORE; HIGH THROUGHPUT SCREENING; HISTOPATHOLOGY; HUMAN; HUMAN TISSUE; MATHEMATICAL MODEL; PRIORITY JOURNAL; PROBABILITY; PROSTATE CANCER; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BIOPSY, NEEDLE; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MALE; MARKOV CHAINS; MODELS, BIOLOGICAL; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; PROSTATIC NEOPLASMS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 77955569128     PISSN: 13618415     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.media.2010.04.007     Document Type: Article

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