Abstract
Diagnosis and grading of breast cancer are done through pathology examination which remains the traditional way in cancer diagnosis. Pathology test is the primary standard for finding severity of abnormality in many cancer diagnoses and also plays the key role in diagnostic assessments. Recently, computerized techniques have been evolving in diagnosing digital pathology for emerging applications related to nuclei detection, segmentation, and classification. Nuclei segmentation remains the challenging task because of cell morphology and architectural distribution. Pathological studies have been conducted for detection and grading many cancers like cervix, lung, brain, prostate and breast cancer and many more. In many of the cancer diagnosis, computer-based diagnostic approaches are playing an important role in reducing human intervention, providing an accurate clinical report. Hence, medical practitioner considers computer-based detection as second opinions. This paper focused on cluster analysis using K-means and classification of breast cancer in the pathological image. The proposed method helps to classify histopathology image in four class by using Support Vector Machine with the combined set of features. The pathology image classified as normal, benign, in situ malignant, invasive malignant by hybrid feature set. The experimental result conducted on publically available dataset INESC-TEC Breast Histology Dataset. The results have proved that the proposed algorithm is suitable for classification of Histopathology image with high accuracy.
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References
Mammography: Medline plus. https://medlineplus.gov/mammography.html. Accessed 09 June 2017
Naik, S., Doyle, S., Agner, S., Madabhushi, A., Feldman, M., Tomaszewski, J.: Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology. In: 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro. ISBI 2008, pp. 284–287. IEEE, May 2008
Xu, J., Xiang, L., Liu, Q., Gilmore, H., Wu, J., Tang, J., Madabhushi, A.: Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images. IEEE Trans. Med. Imaging 35(1), 119–130 (2016)
Gurcan, M.N., Boucheron, L.E., Can, A., Madabhushi, A., Rajpoot, N.M., Yener, B.: Histopathological image analysis: a review. IEEE Rev. Biomed. Eng. 2, 147–171 (2009)
Yamaguchi, M.: Computer-aided differentiation for pathology images. In: Arimura, H. (ed.) Image-Based Computer-Assisted Radiation Therapy, pp. 67–84. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-2945-5_4
Veta, M., et al.: Breast cancer histopathology image analysis: a review. IEEE Trans. Biomed. Eng. 61(5), 1400–1411 (2014)
Irshad, H., Veillard, A., Roux, L., Racoceanu, D.: 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)
He, L., Long, L.R., Antani, S., Thoma, G.R.: Histology image analysis for carcinoma detection and grading. Comput. Methods Programs Biomed. 107(3), 538–556 (2012)
Sakellarios, C., Kostopoulos, S., Glotsos, D., Ravazoula, P., Cavouras, D.: Segmentation and classification of histopathology images for abetting diagnosis in urinary bladder cancer
Diamond, J., Anderson, N.H., Bartels, P.H., Montironi, R., Hamilton, P.W.: The use of morphological characteristics and texture analysis in the identification of tissue composition in prostatic neoplasia. Hum. Pathol. 35(9), 1121–1131 (2004)
Esgiar, A.N., Naguib, R.N., Sharif, B.S., Bennett, M.K., Murray, A.: Microscopic image analysis for quantitative measurement and feature identification of normal and cancerous colonic mucosa. IEEE Trans. Inf. Technol. Biomed. 2(3), 197–203 (1998)
Huang, P.W., Lee, C.H.: Automatic classification for pathological prostate images based on fractal analysis. IEEE Trans. Med. Imaging 28(7), 1037–1050 (2009)
Gilmore, S., Hofmann-Wellenhof, R., Muir, J., Soyer, H.P.: Lacunarity analysis: a promising method for the automated assessment of melanocytic naevi and melanoma. PLoS One 4(10), e7449 (2009)
Loukas, C.G., Wilson, G.D., Vojnovic, B., Linney, A.: An image analysis-based approach for automated counting of cancer cell nuclei in tissue sections. Cytometry Part A 55(1), 30–42 (2003)
Janowczyk, A., Madabhushi, A.: Deep learning for digital pathology image analysis: a comprehensive tutorial with selected use cases. J. Pathol. Inf. 7 (2016)
Veta, M., Huisman, A., Viergever, M.A., van Diest, P.J., Pluim, J.P.: Marker-controlled watershed segmentation of nuclei in H&E stained breast cancer biopsy images. In: 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 618–621. IEEE, March 2011
Araújo, T., et al.: Classification of breast cancer histology images using convolutional neural networks. PLoS One (2017). https://doi.org/10.1371/journal.pone.0177544
Veta, M., van Diest, P.J., Kornegoor, R., Huisman, A., Viergever, M.A., Pluim, J.P.: Automatic nuclei segmentation in H&E stained breast cancer histopathology images. PloS One 8(7), e70221 (2013)
Hartigan, J.A., Wong, M.A.: Algorithm AS 136: a k-means clustering algorithm. J. Roy. Stat. Soc. Series C (Appl. Stat.) 28(1), 100–108 (1979)
Chitade, A.Z., Katiyar, S.K.: Color-based image segmentation using k-means clustering. Int. J. Eng. Sci. Technol. 2(10), 5319–5325 (2010)
Recky, M., Leberl, F.: Windows detection using k-means in CIE-lab color space. In: 2010 20th International Conference on Pattern Recognition (ICPR), pp. 356–359. IEEE, August 2010
Heckbert, P.: Color image quantization for frame buffer display, vol. 16, no. 3, pp. 297–307. ACM (1982)
Santosh, K.C., Wendling, L., Antani, S., Thoma, G.R.: Overlaid arrow detection for labeling regions of interest in biomedical images. IEEE Intell. Syst. 31(3), 66–75 (2016)
Support Vector Machines for Machine Learning. https://machinelearningmastery.com/support-vector-machines-for-machine-learning. Accessed 10 Oct 2018
A tour of the top 10 algorithms for machine learning newbies. https://towardsdatascience.com/a-tour-of-the-top-10-algorithms-for-machine-learning-newbies-dde4edffae11. Accessed 10 Oct 2018
SVM Theory. https://towardsdatascience.com/a-tour-of-the-top-10-algorithms-for-machine-learning-newbies-dde4edffae11. Accessed 10 Oct 2018
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Halalli, B., Makandar, A. (2019). Classification of Pathology Images of Breast Cancer. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1036. Springer, Singapore. https://doi.org/10.1007/978-981-13-9184-2_9
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DOI: https://doi.org/10.1007/978-981-13-9184-2_9
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