High-throughput prostate cancer gland detection, segmentation, and classification from digitized needle core biopsies

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

Volumn 6367 LNCS, Issue , 2010, Pages 77-88

  Xu, Jun ^a   Sparks, Rachel ^a   Janowcyzk, Andrew ^a,c   Tomaszewski, John E ^b   Feldman, Michael D ^b   Madabhushi, Anant ^a  

^a RUTGERS UNIVERSITY   (United States)

^b HOSPITAL OF THE UNIVERSITY OF PENNSYLVANIA   (United States)

^c INDIAN INSTITUTE OF TECHNOLOGY BOMBAY   (India)

Author keywords

digital pathology; geodesic active contour model; glands; High throughput; morphological feature; needle biopsy; prostate cancer

Indexed keywords

DIGITAL PATHOLOGY; GEODESIC ACTIVE CONTOUR MODEL; GLANDS; HIGH-THROUGHPUT; MORPHOLOGICAL FEATURES; NEEDLE BIOPSY; PROSTATE CANCERS;

BIOPSY; DISEASES; EDGE DETECTION; FEATURE EXTRACTION; GEODESY; IMAGE SEGMENTATION; MEDICAL IMAGING; NEEDLES; PATHOLOGY; THROUGHPUT; VECTOR QUANTIZATION; VIDEO SIGNAL PROCESSING;

COMPUTER AIDED DIAGNOSIS;

EID: 78149392546     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15989-3_10     Document Type: Conference Paper

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