Separation of benign and malignant glands in prostatic adenocarcinoma

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

Volumn 8151 LNCS, Issue PART 3, 2013, Pages 461-468

  Rashid, Sabrina ^a   Fazli, Ladan ^a   Boag, Alexander ^b   Siemens, Robert ^b   Abolmaesumi, Purang ^a   Salcudean, Septimiu E ^a  

^a UNIVERSITY OF BRITISH COLUMBIA   (Canada)

^b KINGSTON GENERAL HOSPITAL   (Canada)

Author keywords

epithelial layer; Nuclei layer; prostatic adenocarcinoma

Indexed keywords

CROSS VALIDATION; EPITHELIAL LAYER; HIGH RESOLUTION; MARKER CONTROLLED WATERSHED ALGORITHMS; NOVEL TECHNIQUES; NUCLEI LAYER; PROSTATIC ADENOCARCINOMA; SPATIAL EXTENT;

ARTIFICIAL INTELLIGENCE; COMPUTER SCIENCE; MEDICAL COMPUTING;

ALGORITHMS; MEDICAL IMAGING;

ADENOCARCINOMA; ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; CELL NUCLEUS; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; HUMAN; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; MALE; METHODOLOGY; MICROSCOPY; PATHOLOGY; PROSTATE TUMOR; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL; SUPPORT VECTOR MACHINE;

ADENOCARCINOMA; ALGORITHMS; ARTIFICIAL INTELLIGENCE; CELL NUCLEUS; COMPUTER SIMULATION; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MALE; MICROSCOPY; MODELS, BIOLOGICAL; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; PROSTATIC NEOPLASMS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE; SUPPORT VECTOR MACHINES;

EID: 84885904929     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-40760-4_58     Document Type: Conference Paper

References (11)

1. Bohring, C., Squires, T.: Cancer statistics. CA Cancer J. Clin. 43, 7-26 (1993)
2. Monaco, J.P., Tomaszewski, J.E., Feldman, M.D., Hagemann, I., Moradi, M., Mousavi, P., Boag, A., Davidson, C., Abolmaesumi, P., Madabhushi, A.: Highthroughput detection of prostate cancer in histological sections using probabilistic pairwise markov models. Medical Image Analysis 14(4), 617 (2010)
3. Nguyen, K., Sarkar, A., Jain, A.K.: Structure and context in prostatic gland segmentation and classification. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part I. LNCS, vol. 7510, pp. 115-123. Springer, Heidelberg (2012)
4. Clark, M.D., Askin, F.B., Bagnell, C.R.: Nuclear roundness factor: a quantitative approach to grading in prostate carcinoma, reliability of needle biopsy tissue, and the effect of tumor stage fore usefulness. The Prostate 10, 199-206 (1987)
5. Naik, S., Doyle, S., Feldman, M., Tomaszewski, J., Madabhushi, A.: Gland segmentation and computerized gleason grading of prostate histology by integrating low-, high-level and domain specific information. In: Proc. of 2nd Workshop on Micro. Image Anal. with Applications in Biology (2007)
6. Epstein, J.I., Netto, G.J.: Biopsy interpretation of the prostate. Lippincott Williams & Wilkins (2007)
7. Khouzani, J.K., Zadeh, S.H.: Multiwavelet grading of prostate pathological images. In: Proc. SPIE, vol. 4628, pp. 1130-1138 (2002)
8. Huang, P.W., Lee, C.H.: Automatic classification for pathological prostate images based on fractal analysis. IEEE Transactions on Medical Imaging 28(7), 1037-1050 (2009)
9. Nguyen, K., Sabata, B., Jain, A.K.: Prostate cancer grading: Gland segmentation and structural features. Pattern Recognition Letters 33, 951-961 (2011)
10. Krzanowski, W.: Principles of multivariate analysis. Oxford Uni. Press (1996)
11. Meyer, F.: Topographic distance and watershed lines. Signal Processing 38(1), 113-125 (1994)