MRF-ANN: a machine learning approach for automated ER scoring of breast cancer immunohistochemical images

Journal of Microscopy

Volumn 267, Issue 2, 2017, Pages 117-129

  Mungle, T ^a   Tewary, S ^a   Das, D K ^a   Arun, I ^b   Basak, B ^b   Agarwal, S ^b   Ahmed, R ^b   Chatterjee, S ^b   Chakraborty, C ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY   (India)

^b Tata Medical Center ^*  (India)

Author keywords

Artificial neural network; breast cancer; ER score; immunohistochemistry; Markov random field

Indexed keywords

AUTOMATION; CELLS; COLOR; CYTOLOGY; DECISION MAKING; DECISION SUPPORT SYSTEMS; DISEASES; IMAGE SEGMENTATION; LEARNING SYSTEMS; MARKOV PROCESSES; MAXIMUM PRINCIPLE; MEDICAL IMAGING;

BREAST CANCER; ESTROGEN RECEPTOR; ESTROGEN RECEPTOR SCORE; F MEASURE; HORMONE RECEPTORS; IMMUNOHISTOCHEMISTRY; MACHINE LEARNING APPROACHES; MARKOV RANDOM FIELDS; RECEPTOR CELLS; TIME CONSUMPTION;

NEURAL NETWORKS;

ESTROGEN RECEPTOR; TUMOR MARKER;

ALLRED SCORING SYSTEM; ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOMATION; BREAST CANCER; BREAST CANCER CELL LINE; CANCER CLASSIFICATION; CANCER PATIENT; CELL COUNTING; COMPARATIVE STUDY; COMPUTER ASSISTED DIAGNOSIS; CONTROLLED STUDY; DECISION SUPPORT SYSTEM; HUMAN; HUMAN TISSUE; IMAGE ANALYSIS; IMAGE SEGMENTATION; IMMUNOHISTOCHEMISTRY; MAJOR CLINICAL STUDY; MARKOV CHAIN; MARKOV RANDOM FIELD; PATHOLOGIST; PRIORITY JOURNAL; PROCESS OPTIMIZATION; SCORING SYSTEM; BREAST TUMOR; FEMALE; IMAGE PROCESSING; LABORATORY AUTOMATION; MACHINE LEARNING; PROCEDURES;

AUTOMATION, LABORATORY; BIOMARKERS, TUMOR; BREAST NEOPLASMS; FEMALE; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; IMMUNOHISTOCHEMISTRY; MACHINE LEARNING; NEURAL NETWORKS (COMPUTER); RECEPTORS, ESTROGEN;

EID: 85017400066     PISSN: 00222720     EISSN: 13652818     Source Type: Journal    
DOI: 10.1111/jmi.12552     Document Type: Article

References (31)

1. Allred, D. C. (1998) Assessment of prognostic and predictive factors in breast cancer by immunohistochemistry. Mod Pathol. 11, 155–68.
2. Banic, N. & Loncaric, S. (2014) Improving the White patch method by subsampling. In Proceedings of the IEEE International Conference on Image Processing (ICIP), 2014, IEEE, Paris, France.
3. Besag, J. (1974) Spatial interaction and the statistical analysis of lattice systems. J. Roy. Stat. Soc. B Met. 36, 192–236.
4. Besag, J. (1986) On the statistical analysis of dirty pictures.J. Roy. Stat. Soc. B Met. 48, 259–302.
5. Besag, J. E. & Moran, P. A. (1975) On the estimation and testing of spatial interaction in Gaussian lattice processes. Biometrika 62, 555–562.
6. Blake, A., Kohli, P. & Rother, C. (2011) Markov Random Fields for Vision and Image Processing. Mit Press, Cambridge, UK.
7. Borges, C. F. (1999) On the estimation of Markov random field parameters. IEEE T. Pattern Anal. 21, 216–224.
8. Derin, H. & Elliott, H. (1987) Modeling and segmentation of noisy and textured images using Gibbs random fields. IEEE T. Pattern Anal. 9, 39–55.
9. Gautam, A. & Bhadauria, H. (2014) White blood nucleus extraction using K-Mean clustering and mathematical morphing. In Proceedings of the Fifth International Conference on Next Generation Information Technology Summit (Confluence), 2014, IEEE, Noida, India.
10. Geman, S. & Geman, D. (1984) Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE T. Pattern Anal. 6, 721–741.
11. Gonzalez, R. C. & Woods, R. E. (2002) Digital image processing, 2nd edn. Prentice Hall Pub.
12. Haralick, R. M. & Shapiro, L. G. (1985) Image segmentation techniques. Comput. Vision Graph. 29, 100–132.
13. He, F., Tian, Z., Liu, X. & Duan, X. (2012) A fast edge tracking algorithm for image segmentation using a simple Markov random field model. In Proceedings of the International Conference on Computer Science and Electronics Engineering (ICCSEE), 2012, IEEE, Hangzhou, Zhejiang China.
14. Ibáñez, M. V. & Simó, A. (2003) Parameter estimation in Markov random field image modeling with imperfect observations. A comparative study. Pattern Recogn. Lett. 24, 2377–2389.
15. Jung, C. & Kim, C. (2010) Segmenting clustered nuclei using H-minima transform-based marker extraction and contour parameterization. IEEE T. Bio-Med. Eng. 57, 2600–2604.
16. Kindermann, R. & Snell, J. L. (1980) Markov Random Fields and their Applications. American Mathematical Society, Providence, RI.
17. Ko, C.-C., Tsai, C.-Y., Lin, C.-H. & Liao, K.-S. (2014) A computer-aided diagnosis system of breast intraductal lesion using histopathological images. In Proceedings of the 29th International Conference on Image and Vision Computing, ACM, New Zealand.
18. Lee, H. & Chen, Y.-P. P. (2014) Cell morphology based classification for red cells in blood smear images. Pattern Recogn Lett. 49, 155–161.
19. Liang, Z., Chang-Hua, L., Faning, D., Deng-Feng, C. & Sheng-Jun, X. (2010) Concrete CT Image Segmentation method based on Markov Random Field. In Proceedings of the 29th Chinese Control Conference (CCC), 2010, IEEE, Beijing, China.
20. MacQueen, J. (1967) Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Oakland, CA, Elsevier.
21. Monaco, J. P. & Madabhushi, A. (2012) Class-specific weighting for Markov random field estimation: application to medical image segmentation. Med. Image Anal. 16, 1477–1489.
22. Nazlibilek, S., Karacor, D., Ercan, T., Sazli, M. H., Kalender, O. & Ege, Y. (2014) Automatic segmentation, counting, size determination and classification of white blood cells. Measurement 55, 58–65.
23. Pal, N. R. & Pal, S. K. (1993) A review on image segmentation techniques. Pattern Recogn. 26, 1277–1294.
24. Sokolova, M., Japkowicz, N. & Szpakowicz, S. (2006) Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation. In Proceedings of the AI 2006: Advances in Artificial Intelligence, Springer, Hobart, Australia.
25. Tomari, R., Zakaria, W. N. W., Jamil, M. M. A., Nor, F. M. & Fuad, N. F. N. (2014) Computer aided system for red blood cell classification in blood smear image. Procedia Comput. Sci. 42, 206–213.
26. Tuominen, V. J., Ruotoistenmaki, S., Viitanen, A., Jumppanen, M. & Isola, J. (2010) ImmunoRatio: a publicly available web application for quantitative image analysis of estrogen receptor (ER), progesterone receptor (PR), and Ki-67. Breast Cancer Res. 12, R56.
27. Wang, L., Liu, J. & Li, S. Z. (2000) MRF parameter estimation by MCMC method. Pattern Recogn 33, 1919–1925.
28. Xing, F., Su, H., Neltner, J. & Yang, L. (2014) Automatic Ki-67 counting using robust cell detection and online dictionary learning. IEEE T. Bio-Med. Eng. 61, 859–870.
29. Xu, J., Monaco, J. P. & Madabhushi, A. (2010) Markov random field driven region-based active contour model (MaRACel): application to medical image segmentation. In Proceedings of the Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2010, Springer, Beijing, China.
30. Yu, Y. & Cheng, Q. (2003) MRF parameter estimation by an accelerated method. Pattern Recogn. Lett. 24, 1251–1259.
31. Zhang, Y., Brady, M. & Smith, S. (2001) Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm.IEEE T. Med. Imaging 20, 45–57.