Microscopic images classification for cancer diagnosis

  • Yashwant KurmiEmail author
  • Vijayshri Chaurasia
  • Narayanan Ganesh
  • Abhimanyu Kesharwani
Original Paper


Computer aided diagnosis of cancer is a field of substantial worth in current scenario since approximately 38% population of the world is suffering from the disease. The detection of cancer is based on the observation of deformation in nuclei structure using histopathology slides/images. The proposed technique utilizes nuclei localization prior to classification of histopathology images as benign and malignant. The features used for classification are an ensemble of 150 bag of visual word features, extracted from preprocessed image and 20 handcrafted features, extracted from the internal parts of nuclei using localized histopathology images. The simulation results confirm the superiority of proposed localization based cancer classification method as compared to existing methods of the domain. It has reported average classification accuracy of 95.03% on BreakHis dataset.


Medical imaging Histopathology Histopathology image Feature extraction Image classification 



Autors are thankful to the supporting team of Jawaharlal Nehru Cancer Hospital & Research Center, (JNCH&RC) Bhopal, India. Specially Smt. Asha Joshi (Chairman), Smt. Divya Parashar (CEO & Research Coordinator), Dr. K. V. Pandya (Director), Dr. Pradeep Kolekar (Medical Director) and Mr. Rakesh Joshi (Additional Director), JNCH&RC Bhopal, India, for facilitating to work with patient data for dataset preparation.


  1. 1.
    Jung, C., Kim, C.: Segmenting clustered nuclei using H-minima transform-based marker extraction and contour parameterization. IEEE Trans. Biomed. Eng. 57(10), 2600–2604 (2010)CrossRefGoogle Scholar
  2. 2.
    Wang, P., Hu, X., Li, Y., Liu, Q., Zhu, X.: Automatic cell nuclei segmentation and classification of breast cancer histopathology images. Signal Process. 122, 1–13 (2016)CrossRefGoogle Scholar
  3. 3.
    Wang, Z.: A semi-automatic method for robust and efficient identification of neighboring muscle cells. Pattern Recogn. 53, 300–312 (2016)CrossRefGoogle Scholar
  4. 4.
    Jabeen, A., Riaz, M.M., Iltaf, N., Ghafoor, A.: Image contrast enhancement using weighted transformation function. IEEE Sens. J. 16(20), 7534–7536 (2016)CrossRefGoogle Scholar
  5. 5.
    Nguyen, K., Sarkar, A., Jain, A.K.: Prostate cancer grading: use of graph cut and spatial arrangement of nuclei. IEEE Trans. Med. Imag 33(12), 2254–2270 (2014)CrossRefGoogle Scholar
  6. 6.
    Lu, Z., Carneiro, G., Bradley, A.P., et al.: Evaluation of three algorithms for the segmentation of overlapping cervical cells. IEEE J. Biomed. Health Inf. 21(2), 441–450 (2017)CrossRefGoogle Scholar
  7. 7.
    Spanhol, F.A., Oliveira, L.S., Petitjean, C., Heutte, L.: A dataset for breast cancer histopathological image classification. IEEE Trans. Biomed. Eng. 63(7), 1455–1462 (2016)CrossRefGoogle Scholar
  8. 8.
    Vu, T.H., Mousavi, H.S., et al.: Histopathological image classification using discriminative feature-oriented dictionary learning. IEEE Trans. Med Imag 35(3), 738–751 (2016)CrossRefGoogle Scholar
  9. 9.
    Naylor, P., La, M., Reyal, F., Walter, T.: Segmentation of nuclei in histopathology images by deep regression of the distance map. IEEE Trans. Med. Imag (2018). CrossRefGoogle Scholar
  10. 10.
    Li, X., Plataniotis, K.N.: Circular mixture modeling of color distribution for blind stain separation in pathology images. IEEE J. Biomed. Health Inf. 21(1), 150–161 (2017)CrossRefGoogle Scholar
  11. 11.
    Wang, W., Ozolek, J.A., Slepcev, D., et al.: An optimal transportation approach for nuclear structure-based pathology. IEEE Trans. Med. Imag. 30(3), 621–631 (2011)CrossRefGoogle Scholar
  12. 12.
    Dundar, M.M., Badve, S., Bilgin, G., et al.: Computerized classification of intraductal breast lesions using histopathological images. IEEE Trans. Biomed. Eng. 58(7), 1977–1984 (2011)CrossRefGoogle Scholar
  13. 13.
    Basavanhally, A.N., et al.: Computerized image-based detection and grading of lymphocytic infiltration in HER2 + breast cancer histopathology. IEEE Trans. Biomed. Eng. 57(3), 642–653 (2010)CrossRefGoogle Scholar
  14. 14.
    Manivannan, S., Li, W., Zhang, J., et al.: Structure prediction for gland segmentation with hand-crafted and deep convolutional features. IEEE Trans. Med. Imag 37(1), 210–221 (2018)CrossRefGoogle Scholar
  15. 15.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)zbMATHGoogle Scholar
  16. 16.
    Raji, C.G., Chandra, S.S.V.: Long-term forecasting the survival in liver transplantation using multilayer perceptron networks. IEEE Trans. Syst. Man Cybern. Syst. 47(8), 2318–2329 (2017)CrossRefGoogle Scholar
  17. 17.
    Li, C., Wang, X., Liu, W., et al.: Weakly supervised mitosis detection in breast histopathology images using concentric loss. Med. Image Anal. 53, 165–178 (2019)CrossRefGoogle Scholar
  18. 18.
    Yan, R., Ren, F., Wang, Z., et al.: Breast cancer histopathological image classification using a hybrid deep neural network. Methods (2019)Google Scholar
  19. 19.
    Yang, W., Wang, K., Zuo, W.: Neighborhood component feature selection for high-dimensional data. JCP 7, 161–168 (2012)Google Scholar
  20. 20.
    Klein, A., et al.: Fast Bayesian optimization of machine learning hyperparameters on large datasets, 2016. ArXiv:abs/1605.07079Google Scholar
  21. 21.
    Irshad, H., et al.: Methods for nuclei detection, segmentation, and classification in digital histopathology: a review current status and future potential. IEEE Rev. Biomed. Eng. 7, 97–114 (2014)CrossRefGoogle Scholar
  22. 22.
    Azzopardi, G., Petkov, N.: Contour detection by CORF operator. In: ANN and Machine Learning ICANN 2012, Lecture Notes in Computer Science, vol. 7552, pp. 395–402. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  23. 23.
    Kurmi, Y., Chaurasia, V., Ganesh, N.: Tumor malignancy detection using histopathology imaging. J. Med. Imaging Radiat. Sci. (2019).
  24. 24.
    E. Mercan, S. Aksoy, L. G. Shapiro, et al.: Localization of diagnostically relevant regions of interest in whole slide images. In: 22nd International Conference on Pattern Recognition, Stockholm, pp. 1179–1184 (2014)Google Scholar
  25. 25.
    Riddle, N.C., et al.: Plasticity in patterns of histone modifications and chromosomal proteins in Drosophila heterochromatin. Genome Res. 21(2), 147–163 (2011)CrossRefGoogle Scholar
  26. 26.
    Kvilekval, K., Fedorov, D., Obara, B., et al.: Bisque: a platform for bioimage analysis and management. Bioinformatics 26(4), 544–552 (2010)CrossRefGoogle Scholar
  27. 27.
    Kurmi, Y., Chaurasia, V.: Multifeature-based medical image segmentation. IET Image Proc. 12(8), 1491–1498 (2018)CrossRefGoogle Scholar
  28. 28.
    Chaurasia, V., Chaurasia, V.: Statistical feature extraction based fast fractal image compression. J. Vis. Commun. Image Represent. 41, 87–95 (2016)CrossRefGoogle Scholar
  29. 29.
    Jaccard, P.: The distribution of the flora in the alpine zone. New Phytol. 11, 37–50 (1912)CrossRefGoogle Scholar
  30. 30.
    Taha, A.A., Hanbury, A.: An efficient algorithm for calculating the exact Hausdorff distance. IEEE Trans. Pattern Anal. Mach. Intell. 37(11), 2153–2163 (2015)CrossRefGoogle Scholar
  31. 31.
    Fawcett, T.: An introduction to ROC analysis. Pattern Recog. Lett. 27, 861–874 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Maulana Azad National Institute of TechnologyBhopalIndia
  2. 2.Jawaharlal Nehru Cancer Hospital and Research CenterBhopalIndia
  3. 3.All India Institute of Medical Sciences BhopalBhopalIndia

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