Feature Extraction of Normalized Colorectal Cancer Histopathology Images

  • Alok Kumar Jain
  • Shyam LalEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 904)


This paper presents different types of feature extraction of normalized colorectal cancer histopathology images. These highlights are exceptionally helpful for separating epithelium and stroma in colorectal cancer (CRC) histopathology images. It is also useful for selecting features and its analysis. In this paper, 27 features are extracted in which 5 are the visual texture features and 22 are the other features such as GLCM, run length and intensity-based features to separate epithelium from the stroma of Colorectal Cancer histopathology images. The utilized component has straightforwardly identified with the human recognition which makes it conceivable to distinguish the nearness of tissue based on parameters. The quantity of utilized highlights is little to differentiate the epithelium from the stroma of CRC histopathology images. In the simulation, we use well-defined and verified histopathology images of stroma and epithelium to correctly differentiate epithelium from stroma. The textural features measure provides the excellent result for 16 typical texture patterns. The issue emerges between the human vision, and modernized strategies that are experienced in this examination show the central point in dissecting of the surface. In which, some of them has removed by using better techniques. In conclusion, perception-based features work well in comparison to previously features used. Some modification like colour normalization of epithelium and stroma image and some of the new features are added because the classification of perception-based features is less.


Histopathology images Perception-based feature Textural features Intensity-based features 



This work is supported by a part of Grant of Young Faculty Research Fellowship under Visvesvaraya PhD Scheme for Electronics & IT from Digital India Corporation (formerly Media Lab Asia), A Research & Development Company of Ministry of Communications & Information Technology, Govt. of India.


  1. 1.
    Ferlay, J., et al.: GLOBOCAN 2012 v1.0, Cancer Incidence and Mortality Worldwide: IARC CancerBase No. 11 [Internet]. International Agency for Research on Cancer, Lyon, France (2013)Google Scholar
  2. 2.
    Bianconi, F., et al.: Discrimination between tumour epithelium and stroma via perception-based features. Neurocomputing 154, 119–126 (2015)Google Scholar
  3. 3.
    Nanni, L., Lumini, A., Brahnam, S.: Local binary patterns variants as texture descriptors for medical image analysis. Artif. Intell. Med. 49, 117–125 (2010)CrossRefGoogle Scholar
  4. 4.
    Kylberg, G., Sintorn, I.M.: Evaluation of noise robustness for local binary pattern descriptors in texture classification. EURASIP J. Image Video Process. 17 (2013)Google Scholar
  5. 5.
    Tamura, H., Mori, T., et al.: Textural features corresponding to visual perception. IEEE Trans. Syst. Man Cybern. 8, 460–473 (1978)Google Scholar
  6. 6.
    Linder, N., Konsti, J., Turkki, R., et al.: Identification of tumor epithelium and stroma in tissue micro arrays using texture analysis. Diagn. Pathol. 7(22), 1–11 (2012)Google Scholar
  7. 7.
    Dalal, N., et al.: Histograms of oriented gradients for human detection. In: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005)Google Scholar
  8. 8.
    Haralick, R.M., et al.: Statistical and structural approaches to texture. Proc. IEEE. 67, 786–804 (1979)CrossRefGoogle Scholar
  9. 9.
    Fouad, S., Randell, D., Galton, A., Mehanna, H., Landini, G.: Unsupervised superpixel-based segmentation of histopathological images with consensus clustering. In: Annual Conference on Medical Image Understanding and Analysis MIUA 2017: Medical Image Understanding and Analysis, pp. 767–779 (2017)Google Scholar
  10. 10.
    Demir, C., et al.: Automated cancer diagnosis based on histopathological images: a systematic survey. Technical Report, TR-05–09, pp. 1–16 (2009)Google Scholar
  11. 11.
    de Siquira, F.R., et al.: Multi-scale gray level occurrence matrices for texture description. Neurocomputing 120, 336–345 (2013)CrossRefGoogle Scholar
  12. 12.
    Hamilton, P.W., et al.: Automated location of dysplastic fields in colorectal histology using image texture analysis. J. Pathol. 182, 68–75 (1997)CrossRefGoogle Scholar
  13. 13.
    Weyn, B., et al.: Automated breast tumor diagnosis and grading based on wavelet chromatin texture description. Cytometry 33, 32–40 (1998)CrossRefGoogle Scholar
  14. 14.
    Irshad, H., et al.: Autumated mitosis detection using texture, SIFT features and HMAX biologically inspired approach. J. Pathol. Inform. 1 (2013)Google Scholar
  15. 15.
    Gurcan, M.N., et al.: Histopathological image analysis: a review. IEEE Rev. Biomed. Eng. 2, 147–168 (2009)CrossRefGoogle Scholar
  16. 16.
    Ishikawa, M., et al.: Automatic quantification of morphological features for hepatic trabeculae analysis in stained liver specimens. J. Med. Imaging 3(2), pp. 027502-(1–13) (2016)Google Scholar
  17. 17.
    Web microscope–web-based virtual microscopy, available online at Last accessed on 16 May 2014
  18. 18.
    Li, X., Plataniotis, K.N.: Complete color normalization approach to histopathology images using color cues computed from saturation weighted statistics. IEEE Trans. Bio-med. Eng. 62(7), 1862–1873 (2015)CrossRefGoogle Scholar

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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of E&C EngineeringNational Institute of Technology KarnatakaSurathkal, MangaluruIndia

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