Classification of Breast Lesions using Histopathology Images and Neural Network

Authors(4) :-Sonali Nandish Manoli, Anand Raj Ulle, N.M Nandini, T.S Rekha

Breast cancer occurs when a malignant tumor originates in the breast. As breast tumors mature, they may metastasize to other parts of the body. However, it is important to keep in mind that, if identified and properly treated while still in its early stages, breast cancer can be cured [1].To achieve the above target it is necessary to develop a computer-aided Diagnosis system which helps in better diagnosis of the condition. It can be achieved by using Digital Image Processing techniques to obtain the regions of interest which show extra growth in the breast. So, a system is developed to classify lesions into Benign (non-cancerous) and Malignant (cancerous) condition. To classify the lesions the stain-color is considered as the important criteria to remove the noise from the digital images. To achieve this, initially the region of interest is obtained using k-means clustering and shape features are extracted. The binary image obtained as the result is further given as an input to obtain the regions of interest using the marker-controlled watershed image segmentation approach. The result of the hybrid approach gives us texture features. Further, the combination of these features is considered for classification. The performance measures namely accuracy , sensitivity , specificity , precision of the system are calculated for Na´ve Bayes , Support Vector Machine , Adaptive Boosting , Classification Tree, Random Forest and Feed-Forward Neural Network Classifier.

Authors and Affiliations

Sonali Nandish Manoli
Department of Information Science and Engineering, JSS Science &Technological University, Mysore, Karnataka, India
Anand Raj Ulle
Department of Information Science and Engineering, JSS Science &Technological University, Mysore, Karnataka, India
N.M Nandini
Department of Pathology, JSS Medical College affiliated to JSS University, Mysore, Karnataka, India
T.S Rekha
Department of Pathology, JSS Medical College affiliated to JSS University, Mysore, Karnataka, India

Histopathology, Digital Images, Stain-Color Normalization, Stain-Color Deconvolution, Image Sharpening, K-means, Shape Features, Foreground Markers, Background Markers, Marker-Controlled Watershed, Texture Features, Classifier, Feed-Forward Neural Network.

  1. Shreshtha Malvia,Sarangadhara Appalaraju Bagad,."Epidemiology of breast cancer in Indian women". Asia Pacific Journal on Clinical Oncology,13(4),289-295(2017)
  2. Shahla Masood.,"Cytomorphology of Fibrocystic Change, High-Risk Proliferative Breast Disease and Premalignant Breast Lesions".Clin.Lab.Med25 (4), 713-731(2005).
  3. AsitRanjanMirdha,VenkateshwaranIyer,.K.,KusumKapila,KusumVerma."Value of scoring system in classification of Proliferative Breast Disease on Fine Needle Aspiration Cytology",.Indian.J.Pathol.Microbiol 49(3),334-340(2006).
  4. Nandini,N.M.,Rekha,T.S.,Manjunath,G.V,"Evaluation of Scoring System in Cytological diagnosis and management of breast lesion with review of literature".Indian.J.Cancer 48(2),240-245(2011).
  5. Sheeba,.D.,Chitrakala Sugumar,"Palpable Breast Lesons-Cytomorphological Analysis and Scoring System with HistopathologicalCorrelation", IOSR-JDMS 15(10),25-29(2016).
  6. SmrithiKrishnaCherath,SavithriMoothiringodeChithrabhaum." Evaluation of Masood's and Modified Masood's Scoring System in the Cytological Diagnosis of Palpable BreastLumpAspirates",.J.Clin.Diagn.Res11(4),EC06-EC10(2017).
  7. LukazJelen, A Kruzak and Thomas G Fevens," Classification of Breast Cancer Malignancy Using Cytological Images of Fine Needle Aspiration Biopsies", International Journal of Applied Mathematics and Computer Science 18(1):75-83(2008).
  8. H. Lyon, A. De Leenheer, R. Horobin, W. Lambert, E. Schulte, B. Van Liedekerke, D. Wittekind, "Standardization of reagents and methods used in cytological and histological practice with emphasis on dyes stains and chromogenic reagents", Histochem. J., 26(7), 533-544, (1994).
  9. G Finlayson, S. Hordey, P. Hubel, "Color by correlation: A simple unifying approach to color constancy", IEEE Trans. Pattern Anal. Mach. Intell., 23(11), 1209-1216, Nov. (2001).
  10. P. Hamilton, P. Bartels, D. Thompson, N. Anderson, R. Montironi, "Automated location of dysplastic fields in colorectal histology using image texture analysis", J. Pathol., 182(1), 68-75, (1999).
  11. A. Ruiz, O. Sertel, M. Ujaldon, U. Catalyureko, J. Saltz, M. Gurcan, "Pathological image analysis using the GPU: Stroma classification for neuroblastoma", Proc. IEEE Int. Conf. Bioinformat. Biomed., pp. 78-85, (2007).
  12. H Qureshi, O. Sertel, N. Rajpoot, R. Wilson, M. Gurcan, "Adaptive discriminant wavelet packet transform and local binary patterns for meningioma subtype classification", Proc. Med. Image Comput. Comput.-Assist. Intervention, pp. 196-204, (2008).
  13. A N. Basavanhally, S. Ganesan, S. Agner, J. P. Monaco, M. D. Feldman, J. E. Tomaszewski, G. Bhanot, A. Madabhushi, "Computerized image-based detection and grading of lymphocytic infiltration in HER2+ breast cancer histopathology", IEEE Trans. Biomed. Eng., 57( 3), pp. 642-653, Mar.( 2010)
  14. E. Reinhard, M. Adhikhmin, B. Gooch, P. Shirley, "Color transfer between images", IEEE Comput. Graph. Appl., 21(5), pp. 34-41, ( 2001).
  15. Breast cancer facts & figures 2011-2012, American Cancer Society INC., 1(34), (2011).
  16. C Loukas, SpirosKostopoulos, Anna Tanoglidi, DimitrisGlotsos, C Sfikas, DionisisCavouras, Breast cancer characterization based on image classification of tissue sections visualized under low magnification', Computational and mathematical methods in medicine, (2013).
  17. VetaMitko, Josien PW Pluim, Paul J Van Diest, Max A Viergever, Breast cancer histopathology image analysis: A review', IEEE Transactions on Biomedical Engineering,61(5), 1400-1411, (2014).
  18. Metin N Gurcan, Laura E Boucheron, Ali Can, AnantMadabhushi, Nasir M Rajpoot, BulentYener, Histopathological image analysis: A review', IEEE reviews in biomedical engineering, vol. 2, pp. 147-171, 2009
  19. J. S. R. Jang, C. T. Sun, E. mizutani, Neuro-fuzzy and soft computing; a computational approach to learning and machine intelligence, (1997)
  20. EunKyeong Kim , Hyunhak Cho , Eunseok Jang , SungshinKim,"Color recognition of landmarks using FIS and CIE LAB",,International Conference on Fuzzy Theory and Its Applications (iFuzzy), August (2017).
  21. AnantMadabhushi, George Lee, "Image analysis and machine learning in digital pathology: Challenges and opportunities", Medical Image Analysis vol 33, 170-175,October (2016).
  22. H Irshad, A. Veillard, L. Roux, D. Racoceanu' Methods for nuclei detection, segmentation, and classification in digital histopathology: a review-current status and future potential', IEEE Rev. Biomed. Eng., vol 7, 97-114, (2014).
  23. H Fatakdawala, J. Xu, A. Basavanhally, G. Bhanot, S. Ganesan, M. Feldman, J.E. Tomaszewski, A. Madabhushi,"Expectation-maximization-driven geodesic active contour with overlap resolution (EMaGACOR): application to lymphocyte segmentation on breast cancer histopathology",IEEE Trans. Biomed. Eng., vol 57, 1676-1689, (2010).
  24. D Glotsos, P. Spyridonos, D. Cavouras, P. Ravazoula, P.-A. Dadioti, G. Nikiforidis "Automated segmentation of routinely hematoxylin-eosin-stained microscopic images by combining support vector machine clustering and active contour models", Anal. Quant. Cytol. Histol.,vol 26 , pp. 331-340, (2004).
  25. Wang H., A. Cruz-Roa, A. Basavanhally, H. Gilmore, N. Shih, M. Feldman, J. Tomaszewski, F. Gonzalez, A. Madabhushi,"Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features", J. Med. Imaging Bellingham Wash, vol 1,34003, (2014).
  26. MD. Schnall, Y. Imai, J. Tomaszewski, H.M. Pollack, R.E. Lenkinski, H.Y. Kressel, Prostate cancer: local staging with endorectal surface coil MR imaging, Radiology, vol178 ,797-802, (1991).
  27. A.D. Ward, C. Crukley, C.A. McKenzie, J. Montreuil, E. Gibson, C. Romagnoli, J.A. Gomez, M. Moussa, J. Chin, G. Bauman, A. Fenster, "Prostate: registration of digital histopathologic images to in vivo MR images acquired by using endorectal receive coil Radiology", vol 263 ,856-864, (2012).
  28. R.S. Savage, Y. Yuan,"Predictingchemoinsensitivity in breast cancer with 'omics/digital pathology data fusion", R. Soc.Open Sci., vol 3 (2016),
  29. K Sirinukunwattana, S. Raza, Y.-W. Tsang, D. Snead, I. Cree, N. Rajpoot,"Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images", IEEE Trans. Med. Imaging. (2016)
  30. AH. Beck, A.R. Sangoi, Leung S., R.J. Marinelli, T.O. Nielsen, M.J. van de Vijver, R.B. West, M. van de Rijn, D. Koller,"Systematic analysis of breast cancer morphology uncovers stromal features associated with survival", Sci. Transl. Med., vol 3 , 108-113, (2011)

Publication Details

Published in : Volume 3 | Issue 6 | July-August 2018
Date of Publication : 2018-08-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 641-648
Manuscript Number : CSEIT1836130
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Sonali Nandish Manoli, Anand Raj Ulle, N.M Nandini, T.S Rekha, "Classification of Breast Lesions using Histopathology Images and Neural Network", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 6, pp.641-648, July-August-2018.
Journal URL :

Article Preview