CFP last date
22 April 2024
Call for Paper
May Edition
IJCA solicits high quality original research papers for the upcoming May edition of the journal. The last date of research paper submission is 22 April 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Classification of Histopathological Images based on Modified Clump Splitting Approach

by Anand Raj Ulle, T. N. Nagabhushan, Nandini Manoli
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 182 - Number 18
Year of Publication: 2018
Authors: Anand Raj Ulle, T. N. Nagabhushan, Nandini Manoli
10.5120/ijca2018917888

Anand Raj Ulle, T. N. Nagabhushan, Nandini Manoli . Classification of Histopathological Images based on Modified Clump Splitting Approach. International Journal of Computer Applications. 182, 18 ( Sep 2018), 1-8. DOI=10.5120/ijca2018917888

@article{ 10.5120/ijca2018917888,
author = { Anand Raj Ulle, T. N. Nagabhushan, Nandini Manoli },
title = { Classification of Histopathological Images based on Modified Clump Splitting Approach },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2018 },
volume = { 182 },
number = { 18 },
month = { Sep },
year = { 2018 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume182/number18/29959-2018917888/ },
doi = { 10.5120/ijca2018917888 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:11:45.122174+05:30
%A Anand Raj Ulle
%A T. N. Nagabhushan
%A Nandini Manoli
%T Classification of Histopathological Images based on Modified Clump Splitting Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 182
%N 18
%P 1-8
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The current research work aims to propose an improved clump splitting approach to classify breast cancer lesion based on extracting shape features. Identifying the number of benign and malignant nuclei in a given area of histopathological tissue is very important for the automated grading system. This process becomes difficult due to irregular size and shape of the nuclei leading to clump formation. Therefore, a major challenge lies in accurately separating these nuclei for further processing. Towards this end, there has been a well-focused research on accurate identification and extraction of nuclei based on concavity analysis. From exhaustive experimentations, it is observed that concavity based approaches pose several limitations: like identifying the concave point pair and selecting the valid split lines. Further, It is also observed from the literature that either region or edge based segmentation is the most commonly used method for segmenting nuclei. Experimental analysis showed that under or over-segmentation is the common problem with region-based methods. Since poor, unclear edges, noise and other artefacts are inevitable in histopathological images, the edge based method does not perform well. Therefore in this research work, a combination of both edge and region-based nuclei segmentation is proposed. The performance measure of the proposed method is evaluated on a dataset consisting of 1820 histopathological images. Further, in comparison with the existing methods, the proposed method showed the improved accuracy of 86%. Also, it is clearly seen from the ROC curve that the non-linear SVM outperforms other classifying methods.

References
  1. “Breast cancer statistics,” https://www.breastcancer.org/ symptoms/understand bc/statistics.
  2. “Breast cancer statistics:indian scenario,” http://cancerindia. org.in/statistics/.
  3. D. M. Hyams, E. Schuur, J. Angel Aristizabal, J. E. Bargallo Rocha, C. Cabello, R. Elizalde, L. Garc´ia-Est´evez, H. L. Gomez, A. Katz, and A. Nu˜nez De Pierro, “Selecting postoperative adjuvant systemic therapy for early stage breast cancer: a critical assessment of commercially available gene expression assays,” Journal of surgical oncology, vol. 115, no. 6, pp. 647–662, 2017.
  4. M. Kwa, A. Makris, and F. J. Esteva, “Clinical utility of geneexpression signatures in early stage breast cancer,” Nature Reviews Clinical Oncology, vol. 14, no. 10, p. 595, 2017.
  5. J. A. Sinnott, S. Peisch, S. Tyekucheva, T. A. Gerke, R. T. Lis, J. R. Rider, M. Fiorentino, M. J. Stampfer, L. A. Mucci, M. Loda et al., “Prognostic utility of a new mrna expression signature of gleason score,” Clinical Cancer Research, pp. clincanres–1245, 2016.
  6. T. Gutschner, G. Richtig, M. Haemmerle, and M. Pichler, “From biomarkers to therapeutic targetsthe promises and perils of long non-coding rnas in cancer,” Cancer and Metastasis Reviews, vol. 37, no. 1, pp. 83–105, 2018.
  7. J. L. Connolly, S. J. Schnitt, H. H. Wang, J. A. Longtine, A. Dvorak, and H. F. Dvorak, “Role of the surgical pathologist in the diagnosis and management of the cancer patient,” 2003.
  8. M.-C. Rousselet, S. Michalak, F. Dupr´e, A. Crou´e, P. Bedossa, J.-P. Saint-Andr´e, and P. Cal`es, “Sources of variability in histological scoring of chronic viral hepatitis,” Hepatology, vol. 41, no. 2, pp. 257–264, 2005.
  9. T. A. Ozkan, A. T. Eruyar, O. O. Cebeci, O. Memik, L. Ozcan, and I. Kuskonmaz, “Interobserver variability in gleason histological grading of prostate cancer,” Scandinavian journal of urology, vol. 50, no. 6, pp. 420–424, 2016.
  10. A. Madabhushi, “Digital pathology image analysis: opportunities and challenges,” 2009.
  11. M. Veta, J. P. Pluim, P. J. Van Diest, and M. A. Viergever, “Breast cancer histopathology image analysis: A review,” IEEE Transactions on Biomedical Engineering, vol. 61, no. 5, pp. 1400–1411, 2014.
  12. A. Laurinavicius, A. Laurinaviciene, D. Dasevicius, N. Elie, B. Plancoulaine, C. Bor, and P. Herlin, “Digital image analysis in pathology: benefits and obligation,” Analytical cellular pathology, vol. 35, no. 2, pp. 75–78, 2012.
  13. M. N. Gurcan, L. Boucheron, A. Can, A. Madabhushi, N. Rajpoot, and B. Yener, “Histopathological image analysis: A review,” IEEE reviews in biomedical engineering, vol. 2, p. 147, 2009.
  14. A. Madabhushi and G. Lee, “Image analysis and machine learning in digital pathology: Challenges and opportunities,” 2016.
  15. G. St°alhammar, N. F. Martinez, M. Lippert, N. P. Tobin, I. Mølholm, L. Kis, G. Rosin, M. Rantalainen, L. Pedersen, J. Bergh et al., “Digital image analysis outperforms manual biomarker assessment in breast cancer,” Modern Pathology, vol. 29, no. 4, p. 318, 2016.
  16. H. Irshad, A. Veillard, L. Roux, and D. Racoceanu, “Methods for nuclei detection, segmentation, and classification in digital histopathology: a reviewcurrent status and future potential,” IEEE reviews in biomedical engineering, vol. 7, pp. 97–114, 2014.
  17. S. Kumar, S. H. Ong, S. Ranganath, T. C. Ong, and F. T. Chew, “A rule-based approach for robust clump splitting,” Pattern Recognition, vol. 39, no. 6, pp. 1088–1098, 2006.
  18. X. Bai, C. Sun, and F. Zhou, “Splitting touching cells based on concave points and ellipse fitting,” Pattern recognition, vol. 42, no. 11, pp. 2434–2446, 2009.
  19. W. X.Wang, “Binary image segmentation of aggregates based on polygonal approximation and classification of concavities,” Pattern Recognition, vol. 31, no. 10, pp. 1503–1524, 1998.
  20. A. S. B. Samma, A. Z. Talib, and R. A. Salam, “Combining boundary and skeleton information for convex and concave points detection,” in 2010 Seventh International Conference on Computer Graphics, Imaging and Visualization. IEEE, 2010, pp. 113–117.
  21. W. Wang and H. Song, “Cell cluster image segmentation on form analysis,” in Natural Computation, 2007. ICNC 2007. Third International Conference on, vol. 4. IEEE, 2007, pp. 833–836.
  22. Q. Wen, H. Chang, and B. Parvin, “A delaunay triangulation approach for segmenting clumps of nuclei,” in Biomedical Imaging: From Nano to Macro, 2009. ISBI’09. IEEE International Symposium on. IEEE, 2009, pp. 9–12.
  23. Q. Zhong, P. Zhou, Q. Yao, and K. Mao, “A novel segmentation algorithm for clustered slender-particles,” Computers and Electronics in Agriculture, vol. 69, no. 2, pp. 118–127, 2009.
  24. H. Wang, H. Zhang, and N. Ray, “Clump splitting via bottleneck detection,” in Image Processing (ICIP), 2011 18th IEEE International Conference on. IEEE, 2011, pp. 61–64.
  25. M. Farhan, O. Yli-Harja, and A. Niemist¨o, “A novel method for splitting clumps of convex objects incorporating image intensity and using rectangular window-based concavity pointpair search,” Pattern Recognition, vol. 46, no. 3, pp. 741–751, 2013.
  26. A. LaTorre, L. Alonso-Nanclares, S. Muelas, J. Pe˜na, and J. DeFelipe, “Segmentation of neuronal nuclei based on clump splitting and a two-step binarization of images,” Expert Systems with Applications, vol. 40, no. 16, pp. 6521–6530, 2013.
  27. H. Li, Z. Ji, and H. Yang, “Quantitative characterization of lamellar and equiaxed alpha phases of (α+β ) titanium alloy using a robust approach for touching features splitting,” Materials Characterization, vol. 76, pp. 6–20, 2013.
  28. W. N. Gonc¸alves and O. M. Bruno, “Automatic system for counting cells with elliptical shape,” arXiv preprint arXiv:1201.3109, 2012.
  29. W. Xiong, S.-H. Ong, J.-H. Lim, K. W. Foong, J. Liu, D. Racoceanu, A. G. Chong, and K. S. Tan, “Automatic area classification in peripheral blood smears,” IEEE Transactions on Biomedical Engineering, vol. 57, no. 8, pp. 1982–1990, 2010.
  30. O. Schmitt and M. Hasse, “Morphological multiscale decomposition of connected regions with emphasis on cell clusters,” Computer Vision and Image Understanding, vol. 113, no. 2, pp. 188–201, 2009.
  31. S. Kothari, Q. Chaudry, and M. D. Wang, “Automated cell counting and cluster segmentation using concavity detection and ellipse fitting techniques,” in Biomedical Imaging: From Nano to Macro, 2009. ISBI’09. IEEE International Symposium on. IEEE, 2009, pp. 795–798.
  32. E. Cosatto, M. Miller, H. P. Graf, and J. S. Meyer, “Grading nuclear pleomorphism on histological micrographs,” in Pattern Recognition, 2008. ICPR 2008. 19th International Conference on. IEEE, 2008, pp. 1–4.
  33. H. Kong, M. Gurcan, and K. Belkacem-Boussaid, “Partitioning histopathological images: an integrated framework for supervised color-texture segmentation and cell splitting,” IEEE transactions on medical imaging, vol. 30, no. 9, pp. 1661– 1677, 2011.
  34. H. Fatakdawala, J. Xu, A. Basavanhally, G. Bhanot, S. Ganesan, M. Feldman, J. E. Tomaszewski, and A. Madabhushi, “Expectation–maximization-driven geodesic active contour with overlap resolution (emagacor): Application to lymphocyte segmentation on breast cancer histopathology,” IEEE Transactions on Biomedical Engineering, vol. 57, no. 7, pp. 1676–1689, 2010.
  35. V. V. Makkapati and S. K. Naik, “Clump splitting based on detection of dominant points from contours,” in Automation Science and Engineering, 2009. CASE 2009. IEEE International Conference on. IEEE, 2009, pp. 197–201.
  36. H. Song, Q. Zhao, and Y. Liu, “Splitting touching cells based on concave-point and improved watershed algorithms,” Frontiers of Computer Science, vol. 8, no. 1, pp. 156–162, 2014.
  37. D. Bibin and P. Punitha, “Stained blood cell detection and clumped cell segmentation useful for malaria parasite diagnosis,” in Multimedia processing, communication and computing applications. Springer, 2013, pp. 195–207.
  38. S. K. Adhikari, J. K. Sing, D. K. Basu, and M. Nasipuri, “A spatial fuzzy c-means algorithm with application to mri image segmentation,” in Advances in Pattern Recognition (ICAPR), 2015 Eighth International Conference on. IEEE, 2015, pp. 1–6.
  39. S. Yang, K. Li, Z. Liang, W. Li, and Y. Xue, “A novel cluster validity index for fuzzy c-means algorithm,” Soft Computing, vol. 22, no. 6, pp. 1921–1931, 2018.
  40. H. Kong, M. Gurcan, and K. Belkacem-Boussaid, “Splitting touching-cell clusters on histopathological images,” in Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on. IEEE, 2011, pp. 208–211.
  41. J. Feldman and M. Singh, “Information along contours and object boundaries.” Psychological review, vol. 112, no. 1, p. 243, 2005.
  42. S. Hermann and R. Klette, “A comparative study on 2d curvature estimators,” in Computing: Theory and Applications, 2007. ICCTA’07. International Conference on. IEEE, 2007, pp. 584–589.
  43. F. A. Spanhol, L. S. Oliveira, C. Petitjean, and L. Heutte, “A dataset for breast cancer histopathological image classification,” IEEE Transactions on Biomedical Engineering, vol. 63, no. 7, pp. 1455–1462, 2016.
  44. T. Rekha, N. Nandini, and M. Dhar, “Expansion of masood’s cytologic index for breast carcinoma and its validity,” Journal of Cytology/Indian Academy of Cytologists, vol. 30, no. 4, p. 233, 2013.
  45. S. K. Cherath and S. M. Chithrabhanu, “Evaluation of masoods and modified masoods scoring systems in the cytological diagnosis of palpable breast lump aspirates,” Journal of clinical and diagnostic research: JCDR, vol. 11, no. 4, p. EC06, 2017.
  46. H. A. Phoulady, D. B. Goldgof, L. O. Hall, and P. R. Mouton, “Nucleus segmentation in histology images with hierarchical multilevel thresholding,” in Medical Imaging 2016: Digital Pathology, vol. 9791. International Society for Optics and Photonics, 2016, p. 979111.
Index Terms

Computer Science
Information Sciences

Keywords

Histopathological Images clumps Shape features nuclei extraction Digital Pathology

Powered by PhDFocusTM