Microscopic images classification for cancer diagnosis


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.

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  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)

    Article  Google 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)

    Article  Google Scholar 

  3. 3.

    Wang, Z.: A semi-automatic method for robust and efficient identification of neighboring muscle cells. Pattern Recogn. 53, 300–312 (2016)

    Article  Google 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)

    Article  Google 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)

    Article  Google 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)

    Article  Google 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)

    Article  Google 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)

    Article  Google 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). https://doi.org/10.21227/H26X0H

    Article  Google 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)

    Article  Google 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)

    Article  Google 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)

    Article  Google 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)

    Article  Google 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)

    Article  Google Scholar 

  15. 15.

    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)

    MATH  Google 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)

    Article  Google 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)

    Article  Google Scholar 

  18. 18.

    Yan, R., Ren, F., Wang, Z., et al.: Breast cancer histopathological image classification using a hybrid deep neural network. Methods (2019)

  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/1605.07079

  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)

    Article  Google 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)

  23. 23.

    Kurmi, Y., Chaurasia, V., Ganesh, N.: Tumor malignancy detection using histopathology imaging. J. Med. Imaging Radiat. Sci. (2019). https://doi.org/10.1016/j.jmir.2019.07.004

  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)

  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)

    Article  Google 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)

    Article  Google Scholar 

  27. 27.

    Kurmi, Y., Chaurasia, V.: Multifeature-based medical image segmentation. IET Image Proc. 12(8), 1491–1498 (2018)

    Article  Google Scholar 

  28. 28.

    Chaurasia, V., Chaurasia, V.: Statistical feature extraction based fast fractal image compression. J. Vis. Commun. Image Represent. 41, 87–95 (2016)

    Article  Google Scholar 

  29. 29.

    Jaccard, P.: The distribution of the flora in the alpine zone. New Phytol. 11, 37–50 (1912)

    Article  Google 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)

    Article  Google Scholar 

  31. 31.

    Fawcett, T.: An introduction to ROC analysis. Pattern Recog. Lett. 27, 861–874 (2006)

    Article  Google Scholar 

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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.

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Correspondence to Yashwant Kurmi.

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Kurmi, Y., Chaurasia, V., Ganesh, N. et al. Microscopic images classification for cancer diagnosis. SIViP 14, 665–673 (2020). https://doi.org/10.1007/s11760-019-01584-4

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  • Medical imaging
  • Histopathology
  • Histopathology image
  • Feature extraction
  • Image classification