A Novel Probabilistic Nuclei Segmentation Algorithm for H&E Stained Histopathological Tissue Images

Abstract

In this study, we propose a novel, fast and accurate segmentation algorithm to segment nuclei in H&E stained histopathological tissue images. The proposed algorithm doesn’t require pre-processing, post-processing, and any manual parameter or threshold. The algorithm utilizes probabilistic and statistical properties of the pixels’ color value in the images with RGB color, and determines whether pixels are a part of any nuclei or not by using an automatically calculated threshold value. The algorithm provides time efficiency and reduced overall cost in the segmentation. The other contributions of the study are false positive removal algorithm and automatically determination of nuclei cluster for K-means. In order to compare and evaluate the performance of the proposed algorithm in terms of time and cost efficiency, K-Means is preferred because of its common usage. Expert evaluation is declared as ground truth for determining the accuracy of the results. The experiments are performed on 60 healthy and 60 damaged kidney, and 60 healthy and 60 damaged liver tissue images. The evaluations are revealed that the proposed algorithm can effectively segment nuclei. The comparison results also demonstrate that the deviation between proposed algorithm and the expert is 2%, while the deviation between K-Means and Expert is 5%.

Keywords

• Image segmentation,biomedical image processing,clustering methods,pattern recognition

A Novel Probabilistic Nuclei Segmentation Algorithm for H&E Stained Histopathological Tissue Images

Abstract

In this study, we propose a novel, fast and accurate segmentation algorithm to segment nuclei in H&E stained histopathological tissue images. The proposed algorithm does not require pre-processing, post-processing, and any manual parameter or threshold. The algorithm utilizes probabilistic and statistical properties of the pixels’ color value in the images with RGB color space, and determines whether pixels are a part of any nuclei or not by using an automatically calculated threshold value. The algorithm provides time efficiency and reduced overall cost in the segmentation. Two more algorithms are also proposed to distinguish nuclei cluster from the other clusters obtained by K-means, and eliminate false positives in nuclei cluster, which are not nuclei. In order to compare and evaluate the performance of the proposed segmentation algorithm in terms of time and cost efficiency, K-Means is preferred because of its common usage. Expert evaluation is declared as ground truth for determining the accuracy of the results. The experiments are performed on 60 healthy and 60 damaged kidney, and 60 healthy and 60 damaged liver tissue images. The evaluations show that the proposed algorithm can effectively segment nuclei. The comparison results also demonstrate that the deviation between proposed algorithm and the expert is 2%, while the deviation between K-Means and expert is 5%.

Keywords

• medical image processing,clustering methods,pattern recognition,Image segmentation,pattern recognition

References

1. S. E. Mills, Histology for pathologists. Philadelphia: Wolters Kluwer Health/Lippincott Williams & Wilkins, 2012.
2. K. S. Suvarna, C. Layton, and J. D. Bancroft, Bancroft’s Theory and Practice of Histological Techniques. Elsevier Health Sciences UK, 2012.
3. L. He, L. R. Long, S. Antani, and G. Thoma, “Computer assisted diagnosis in histopathology,” Seq. Genome Anal. Methods Appl., pp. 271–287, 2010.
4. H. Fox, “Is H&E morphology coming to an end?,” J. Clin. Pathol., vol. 53, no. 1, pp. 38–40, Jan. 2000.
5. D. B. Murphy and M. W. Davidson, Fundamentals of light microscopy and electronic imaging. Hoboken, N.J.: Wiley-Blackwell, 2012.
6. G. D. Thomas, M. F. Dixon, N. C. Smeeton, and N. S. Williams, “Observer variation in the histological grading of rectal carcinoma.,” J. Clin. Pathol., vol. 36, no. 4, pp. 385–391, Apr. 1983.
7. G. E. Metter et al., “Morphological subclassification of follicular lymphoma: variability of diagnoses among hematopathologists, a collaborative study between the Repository Center and Pathology Panel for Lymphoma Clinical Studies.,” J. Clin. Oncol., vol. 3, no. 1, pp. 25–38, Jan. 1985.
8. F. Dick et al., “Use of the Working Formulation for Non-Hodgkin’s Lymphoma in Epidemiologic Studies: Agreement Between Reported Diagnoses and a Panel of Experienced Pathologists,” J. Natl. Cancer Inst., vol. 78, no. 6, pp. 1137–1144, Jan. 1987.

9. W. C. Chan et al., “A clinical evaluation of the International Lymphoma Study Group classification of non-Hodgkin’s lymphoma,” Blood, vol. 89, no. 11, pp. 3909–3918, 1997.
10. F. Serin, M. Ertürkler, and M. Gül, “K-nearest unrepeatable cell graph model of histopathological tissue image,” in 2015 23nd Signal Processing and Communications Applications Conference (SIU), 2015, pp. 2585–2588.
11. F. Serin, M. Erturkler, and M. Gul, “A novel overlapped nuclei splitting algorithm for histopathological images,” Comput. Methods Programs Biomed., vol. 151, pp. 57–70, Nov. 2017.
12. C. Gunduz, B. Yener, and S. H. Gultekin, “The cell graphs of cancer,” Bioinformatics, vol. 20, no. suppl 1, pp. i145–i151, Aug. 2004.
13. H. P. Ng, S. H. Ong, K. W. C. Foong, P. S. Goh, and W. L. Nowinski, “Medical image segmentation using K-means clustering and improved watershed algorithm,” 2006, pp. 61–65.
14. S. Petushi, F. U. Garcia, M. M. Haber, C. Katsinis, and A. Tozeren, “Large-scale computations on histology images reveal grade-differentiating parameters for breast cancer,” BMC Med. Imaging, vol. 6, no. 1, p. 14, Oct. 2006.
15. C. Bilgin, C. Demir, C. Nagi, and B. Yener, “Cell-Graph Mining for Breast Tissue Modeling and Classification,” in 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007, pp. 5311–5314.
16. M. N. Gurcan, L. E. Boucheron, A. Can, A. Madabhushi, N. M. Rajpoot, and B. Yener, “Histopathological image analysis: A review,” Biomed. Eng. IEEE Rev. In, vol. 2, pp. 147–171, 2009.
17. S. Kothari, Q. Chaudry, and M. D. Wang, “Automated cell counting and cluster segmentation using concavity detection and ellipse fitting techniques,” presented at the IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009. ISBI ’09, 2009, pp. 795–798.
18. C. C. Bilgin, P. Bullough, G. E. Plopper, and B. Yener, “ECM-aware cell-graph mining for bone tissue modeling and classification,” Data Min. Knowl. Discov., vol. 20, no. 3, pp. 416–438, 2010.
19. G. Malu, K. Balakrishnan, and N. K. Bodhey, “Area and volume calculation of necrotic tissue regions of heart using interpolation,” in 2011 International Conference on Emerging Trends in Electrical and Computer Technology (ICETECT), 2011, pp. 728–730.
20. M. Baykara, M. Erturkler, M. Gul, and M. Harputluoglu, “Karaciğer Dokusundaki Nekroz Alanın Doku Tabanlı Bölütleme Kullanılarak Belirlenmesi ve Nicemlenmesi,” presented at the Akıllı Sistemlerde Yenilikler ve Uygulamaları Sempozyumu (ASYU), Trabzon/Turkey, 2012.
21. T. Ozseven, M. Erturkler, M. Nurmuhammed, M. Gul, and M. Harputluoglu, “Quantifying the necrotic areas on liver tissues using support vector machine (SVM) algorithm and Gabor filters,” in 2012 International Symposium on Innovations in Intelligent Systems and Applications (INISTA), 2012, pp. 1–5.
22. F. Serin, M. Erturkler, M. Gul, and B. Yigitcan, “Non-Alkolik Yağlı Karaciğer Hastalığında Karaciğerdeki Yağ Vakuolleri Oranının Hesaplanması,” presented at the Akıllı Sistemlerde Yenilikler ve Uygulamaları Sempozyumu, 2012, pp. 306–310.
23. F. Serin, M. Erturkler, M. Gul, and B. Yigitcan, “Investigating the effects of melatonin and resveratrol agents on non-alcoholic fatty liver disease,” Glob. J. Technol., vol. 3, Jun. 2013.
24. A. Skodras, S. Giannarou, M. Fenwick, S. Franks, J. Stark, and K. Hardy, “Object recognition in the ovary: Quantification of oocytes from microscopic images,” in 2009 16th International Conference on Digital Signal Processing, 2009, pp. 1–6.
25. W.-Y. Chang et al., “Computer-Aided Diagnosis of Skin Lesions Using Conventional Digital Photography: A Reliability and Feasibility Study,” PLOS ONE, vol. 8, no. 11, p. e76212, Nov. 2013.
26. M. Veta, J. P. W. Pluim, P. J. van Diest, and M. A. Viergever, “Breast Cancer Histopathology Image Analysis: A Review,” IEEE Trans. Biomed. Eng., vol. 61, no. 5, pp. 1400–1411, May 2014.
27. S. Wang et al., “Computer Aided-Diagnosis of Prostate Cancer on Multiparametric MRI: A Technical Review of Current Research, Computer Aided-Diagnosis of Prostate Cancer on Multiparametric MRI: A Technical Review of Current Research,” BioMed Res. Int. BioMed Res. Int., vol. 2014, 2014, p. e789561, Dec. 2014.
28. M. Firmino, A. H. Morais, R. M. Mendoça, M. Dantas, H. Hekis, and R. Valentim, “Computer-aided detection system for lung cancer in computed tomography scans: Review and future prospects,” Biomed Eng Online, vol. 13, pp. 1–16, 2014.
29. N. Otsu, “A threshold selection method from gray-level histograms,” Automatica, vol. 11, no. 285–296, pp. 23–27, 1975.
30. R. Adams and L. Bischof, “Seeded region growing,” Pattern Anal. Mach. Intell. IEEE Trans. On, vol. 16, no. 6, pp. 641–647, 1994.
31. [31] D. D. Patil and S. G. Deore, “Medical image segmentation: a review,” Int. J. Comput. Sci. Mob. Comput., vol. 2, no. 1, pp. 22–27, 2013.
32. C. Zhang et al., “White Blood Cell Segmentation by Color-Space-Based K-Means Clustering,” Sensors, vol. 14, no. 9, pp. 16128–16147, Sep. 2014.
33. D.-Q. Zhang and S.-C. Chen, “A novel kernelized fuzzy c-means algorithm with application in medical image segmentation,” Artif. Intell. Med., vol. 32, no. 1, pp. 37–50, 2004.
34. K.-S. Chuang, H.-L. Tzeng, S. Chen, J. Wu, and T.-J. Chen, “Fuzzy c-means clustering with spatial information for image segmentation,” Comput. Med. Imaging Graph., vol. 30, no. 1, pp. 9–15, 2006.
35. H. Kong, K. Belkacem-Boussaid, and M. Gurcan, “Cell nuclei segmentation for histopathological image analysis,” in SPIE Medical Imaging, 2011, pp. 79622R–79622R.
36. X. Zhang, F. Xing, H. Su, L. Yang, and S. Zhang, “High-throughput histopathological image analysis via robust cell segmentation and hashing,” Med. Image Anal., vol. 26, no. 1, pp. 306–315, Aralık 2015.
37. Y. Xu, J.-Y. Zhu, E. I.-C. Chang, M. Lai, and Z. Tu, “Weakly supervised histopathology cancer image segmentation and classification,” Med. Image Anal., vol. 18, no. 3, pp. 591–604, Nisan 2014.
38. S. Wienert et al., “Detection and segmentation of cell nuclei in virtual microscopy images: a minimum-model approach,” Sci. Rep., vol. 2, p. 503, 2012.
39. Y. Al-Kofahi, W. Lassoued, W. Lee, and B. Roysam, “Improved Automatic Detection and Segmentation of Cell Nuclei in Histopathology Images,” IEEE Trans. Biomed. Eng., vol. 57, no. 4, pp. 841–852, Apr. 2010.
40. S. S. Kecheril, D. Venkataraman, J. Suganthi, and K. Sujathan, “Automated lung cancer detection by the analysis of glandular cells in sputum cytology images using scale space features,” Signal Image Video Process., vol. 9, no. 4, pp. 851–863, Jun. 2013.
41. S. Kothari, J. H. Phan, T. H. Stokes, and M. D. Wang, “Pathology imaging informatics for quantitative analysis of whole-slide images,” J. Am. Med. Inform. Assoc., vol. 20, no. 6, pp. 1099–1108, Nov. 2013.
42. S. Ray and R. H. Turi, “Determination of number of clusters in k-means clustering and application in colour image segmentation,” in Proceedings of the 4th international conference on advances in pattern recognition and digital techniques, 1999, pp. 137–143.
43. L. He, Y. Chao, and K. Suzuki, “A Run-Based Two-Scan Labeling Algorithm,” IEEE Trans. Image Process., vol. 17, no. 5, pp. 749–756, May 2008.