A Stochastic Polygons Model for Glandular Structures in Colon Histology Images

IEEE Transactions on Medical Imaging

Volumn 34, Issue 11, 2015, Pages 2366-2378

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

^a UNIVERSITY OF WARWICK   (United Kingdom)

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

^c Qatar University ^*  (Qatar)

Author keywords

Bayesian inference; gland modeling; histology image analysis; random polygons; reversible jump Markov chain Monte Carlo

Indexed keywords

BAYESIAN NETWORKS; CHAINS; GEOMETRY; HISTOLOGY; INFERENCE ENGINES; INTELLIGENT SYSTEMS; MARKOV PROCESSES; MONTE CARLO METHODS; STOCHASTIC SYSTEMS; TISSUE;

BAYESIAN INFERENCE; CANCEROUS TISSUES; GLAND SEGMENTATIONS; GLANDULAR STRUCTURES; HISTOLOGY IMAGES; RANDOM POLYGON; REVERSIBLE JUMP MARKOV CHAIN MONTE CARLO; UNDIFFERENTIATED CARCINOMAS;

STOCHASTIC MODELS;

ALGORITHM; ARTICLE; BAYES THEOREM; CANCER GRADING; COLON; COLON ADENOCARCINOMA; COLON ADENOMA; CONTROLLED STUDY; HISTOLOGY; HUMAN; HUMAN TISSUE; IMAGE ANALYSIS; MONTE CARLO METHOD; QUALITATIVE ANALYSIS; QUANTITATIVE ANALYSIS; STOCHASTIC MODEL; BIOLOGICAL MODEL; CHEMISTRY; COLON TUMOR; CYTOCHEMISTRY; IMAGE PROCESSING; INTESTINE MUCOSA; MICROSCOPY; PATHOLOGY; PROCEDURES; STATISTICAL MODEL;

COLON; COLONIC NEOPLASMS; HISTOCYTOCHEMISTRY; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; INTESTINAL MUCOSA; MICROSCOPY; MODELS, BIOLOGICAL; MODELS, STATISTICAL;

EID: 84955289509     PISSN: 02780062     EISSN: 1558254X     Source Type: Journal    
DOI: 10.1109/TMI.2015.2433900     Document Type: Article

References (29)

1. C. W. Elston and I. O. Ellis, "Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: Experience from a large study with long-term follow-up," Histopathology, vol. 19, no. 5, pp. 403-410, 1991.
2. D. F. Gleason and G. T. Mellinger, "Prediction of prognosis for prostatic adenocarcinoma by combined histological grading and clinical staging," J. Urol., vol. 167, no. 2, pp. 953-958, 2002.
3. P. A. Humphrey, "Gleason grading and prognostic factors in carcinoma of the prostate," Modern Pathol., vol. 17, no. 3, pp. 292-306, 2004.
4. S. R. Hamilton and L. A. Aaltonen, "," in Pathology and Genetics of Tumours of the Digestive System. Lyon Cedex, France: IARC Press, 2000.
5. K. Derwinger, K. Kodeda, E. Bexe-Lindskog, and H. Taflin, "Tumour differentiation grade is associated with TNM staging and the risk of node metastasis in colorectal cancer," Acta Oncologica, vol. 49, no. 1, pp. 57-62, 2009.
6. M. McCann, J. Ozolek, C. Castro, B. Parvin, and J. Kovacevic, "Automated histology analysis: Opportunities for signal processing," IEEE Signal Process. Mag., vol. 32, no. 1, pp. 78-87, Jan. 2015.
7. M. N. Gurcan et al., "Histopathological image analysis: A review," IEEE Rev. Biomed. Eng., vol. 2, pp. 147-171, 2009.
8. T. J. Fuchs and J. M. Buhmann, "Computational pathology: Challenges and promises for tissue analysis," Comput.Med. Imag. Graph., vol. 35, no. 7, pp. 515-530, 2011.
9. M. Fleming, S. Ravula, S. F. Tatishchev, and H. L. Wang, "Colorectal carcinoma: Pathologic aspects," J. Gastrointest. Oncol., vol. 3, no. 3, pp. 153-173, 2012.
10. P. Green, "Reversible jump Markov chain Monte Carlo computation and Bayesian model determination," Biometrika, vol. 82, no. 4, pp. 711-732, 1995.
11. W. K. Hastings, "Monte Carlo sampling methods using Markov chains and their applications," Biometrika, vol. 57, no. 1, pp. 97-109, 1970.
12. R. Farjam, H. Soltanian-Zadeh, K. Jafari-Khouzani, and R. Zoroofi, "An image analysis approach for automatic malignancy determination of prostate pathological images," Cytometry Part B, Clin. Cytometry, vol. 72, no. 4, pp. 227-240, 2007.
13. 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 Microscopic Image Analysis Appl. Biol., 2007.
14. K. Nguyen, A. Jain, and R. Allen, "Automated gland segmentation and classification for Gleason grading of prostate tissue images," in Proc. IEEE Int. Conf. Pattern Recognit., 2010, pp. 1497-1500.
15. C. Gunduz-Demir, M. Kandemir, A. Tosun, and C. Sokmensuer, "Automatic segmentation of colon glands using object-graphs," Med. Image Anal., vol. 14, no. 1, pp. 1-12, 2010.
16. A. Fakhrzadeh, E. Spörndly-Nees, L. Holm, and C. L. L. Hendriks, "Analyzing tubular tissue in histopathological thin sections," in Proc. IEEE Int. Conf. Digital Image Comput. Tech. Appl., 2012, pp. 1-6.
17. H. Fu, G. Qiu, J. Shu, and M. Ilyas, "A novel polar space random field model for the detection of glandular structures," IEEE Trans. Med. Imag., vol. 33, no. 3, pp. 764-776, Mar. 2014.
18. B. Sabata, B. Babenko, R. Monroe, and C. Srinivas, "Automated analysis of PIN-4 stained prostate needle biopsies," in Prostate Cancer Imag., Computer-Aided Diagnosis, Prognosis, Intervent., 2010, pp. 89-100.
19. I. L. Dryden, M. R. Scarr, and C. C. Taylor, "Bayesian texture segmentation of weed and crop images using reversible jump Markov chain Monte Carlo methods," J. R. Stat. Soc., Ser. C Appl. Stat., vol. 52, no. 1, pp. 31-50, 2003.
20. I. L. Dryden, R. Farnoosh, and C. C. Taylor, "Image segmentation using Voronoi polygons and MCMC, with application to muscle fibre images," J. Appl. Stat., vol. 33, no. 6, pp. 609-622, 2006.
21. Y. Li, J. Li, and M. A. Chapman, "Segmentation of SAR intensity imagery with a Voronoi tessellation, Bayesian inference, and reversible jump MCMC algorithm," IEEE Trans. Geosci. Remote Sens., vol. 48, no. 4, pp. 1872-1881, Apr. 2010.
22. K. Sirinukunwattana, D. Snead, and N. Rajpoot, "A novel texture descriptor for detection of glandular structures in colon histology images," in Proc. SPIE Med. Imag., 2015, pp. 94 200S-94 200S.
23. A. Lucchi, K. Smith, R. Achanta, V. Lepetit, and P. Fua, "A fully automated approach to segmentation of irregularly shaped cellular structures in EM images," in Proc. MICCAI, 2010, pp. 463-471.
24. R. Achanta et al., "SLIC superpixels compared to state-of-the-art superpixel methods," IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 11, pp. 2274-2282, Nov. 2012.
25. M. Macenko et al., "A method for normalizing histology slides for quantitative analysis," in Proc. Int. Symp. Biomed. Imag., 2009, vol. 9, pp. 1107-1110.
26. N. Otsu, "A threshold selection method from gray-level histograms," IEEE Trans. Syst., Man Cybern., vol. 9, no. 1, pp. 62-66, 1979.
27. A. Khan, N. Rajpoot, D. Treanor, and D. Magee, "A nonlinear mapping approach to stain normalization in digital histopathology images using image-specific color deconvolution," IEEE Trans. Biomed. Eng., vol. 61, no. 6, pp. 1729-1738, Jun. 2014.
28. P. Jaccard, "Étude comparative de la distribution florale dans une portion des Alpes et des Jura," Bulletin de la Société Vaudoise des Sciences Naturelles, vol. 37, pp. 547-579, 1901.
29. L. R. Dice, "Measures of the amount of ecologic association between species," Ecology, vol. 26, no. 3, pp. 297-302, 1945.