A random polygons model of glandular structures in colon histology images

Proceedings - International Symposium on Biomedical Imaging

Volumn 2015-July, Issue , 2015, Pages 1526-1529

  Sirinukunwattana, Korsuk ^a,b   Snead, David R J ^c   Rajpoot, Nasir M ^a  

^a Qatar University ^*  (Qatar)

^b UNIVERSITY OF WARWICK   (United Kingdom)

^c UNIVERSITY HOSPITALS COVENTRY AND WARWICKSHIRE NHS TRUST   (United Kingdom)

Author keywords

Bayesian inference; Gland modeling; Histology image analysis; Random polygons; Reversible Jump Markov chain Monte Carlo

Indexed keywords

BAYESIAN NETWORKS; GEOMETRY; HISTOLOGY; INFERENCE ENGINES; MARKOV CHAINS; MEDICAL IMAGING; MONTE CARLO METHODS; STOCHASTIC SYSTEMS; TISSUE;

BAYESIAN INFERENCE; COLON TISSUES; GLANDULAR STRUCTURES; HISTOLOGY IMAGES; HUMAN COLON; RANDOM NUMBERS; RANDOM POLYGON; REVERSIBLE JUMP MARKOV CHAIN MONTE CARLO;

STOCHASTIC MODELS;

EID: 84944328006     PISSN: 19457928     EISSN: 19458452     Source Type: Conference Proceeding    
DOI: 10.1109/ISBI.2015.7164168     Document Type: Conference Paper

References (11)

1. D. F. Gleason and G. T. Mellinger, "Prediction of prognosis for prostatic adenocarcinoma by combined histological grading and clinical staging," The Journal of Urology, vol. 167, no. 2, pp. 953-958, 2002.
2. S. R. Hamilton, L. A. Aaltonen, International Agency for Research on Cancer, World Health Organization, and others, Pathology and genetics of tumours of the digestive system, 2000.
3. R. Farjam, H. Soltanian-Zadeh, K. Jafari-Khouzani, and R. A. Zoroofi, "An image analysis approach for automatic malignancy determination of prostate pathological images," Cytometry Part B: Clinical Cytometry, vol. 72, no. 4, pp. 227-240, 2007.
4. S. Naik, S. Doyle, A. Madabhushi, J. Tomaszewski, and M. Feldman, "Gland segmentation and gleason grading of prostate histology by integrating low-, high-level and domain specific information," in Workshop on Microscopic Image Analysis with Applications in Biology, 2007.
5. K. Nguyen, A. K. Jain, and R. L. Allen, "Automated gland segmentation and classification for Gleason grading of prostate tissue images," in Pattern Recognition(ICPR), 2010 20th International Conference on. IEEE, 2010, pp. 1497-1500.
6. C. Gunduz-Demir, M. Kandemir, A. B. Tosun, and C. Sokmensuer, "Automatic segmentation of colon glands using objectgraphs," Medical Image Analysis, vol. 14, no. 1, pp. 1-12, 2010.
7. P. J. Green, "Reversible jump Markov chain Monte Carlo computation and Bayesian model determination," Biometrika, vol. 82, no. 4, pp. 711-732, 1995.
8. K. Sirinukunwattana, D. S. Snead, and N. M. Rajpoot, "A Novel Texture Descriptor for Detection of Glandular Structures in Colon Histology Images," in accepted in SPIE Medical Imaging, 2015.
9. R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, "SLIC superpixels compared to state-of-the-art superpixel methods," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 11, pp. 2274-2282, 2012.
10. M. Macenko, M. Niethammer, J. S. Marron, D. Borland, J. T. Woosley, X. Guan, C. Schmitt, and N. E. Thomas, "A Method for Normalizing Histology Slides for Quantitative Analysis.," in ISBI, 2009, vol. 9, pp. 1107-1110.
11. A. M. Khan, N. M. Rajpoot, D. Treanor, and D. Magee, "A nonlinear mapping approach to stain normalization in digital histopathology images using image-specific color deconvolution," IEEE Transactions on Biomedical Engineering, vol. 61, no. 6, pp. 1729-1738, 2014.