Design and Analysis of an Isotropic Wavelet Features-Based Classification Algorithm for Adenocarcinoma and Squamous Cell Carcinoma of Lung Histological Images

  • Manas Jyoti DasEmail author
  • Lipi B. Mahanta
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11942)


One of the most prevailing types of lung cancer is non-small cell lung cancer (NSCLC). Differential diagnosis of NSCLC into adenocarcinoma (ADC) and squamous cell carcinoma (SCC) is important because of prognosis. Histological images are taken from a database consisting of 72 lung tissue samples collected indigenously with a core needle biopsy. In this work, a novel method has been developed where the features of ADC and SCC for a histological image are taken from various statistical and mathematical models implemented on the coefficients of the wavelet transform of an image. The method provides a precision of 95.1% and 96.2% in classifying malignant and non-malignant tissue type respectively. This methodology of classifying ADC and SCC without coding clinical diagnostic features into the system is a necessary step forward towards an autonomous decision system.


Adenocarcinoma Squamous cell carcinoma Histological Wavelet Colour transformation L*a*b* 


  1. 1.
    Bray, F., Jacques, F., et al.: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 68, 394–424 (2018)CrossRefGoogle Scholar
  2. 2.
    Ma, L.H., Li, G., et al.: The effect of nonsmall cell lung cancer histology on survival as measured by the graded prognostic assessment in patients with brain metastases treated by hypofractionated stereotactic radiotherapy. Radiat. Oncol. 11, 92 (2016)CrossRefGoogle Scholar
  3. 3.
    Yano, M., Yoshida, J., et al.: The outcomes of a limited resection for nonsmall cell lung cancer based on differences in pathology. World J. Surg. 40(11), 2688–2697 (2016)CrossRefGoogle Scholar
  4. 4.
    Yao, X., Gomes, M.M., et al.: Fine-needle aspiration biopsy versus core-needle biopsy in diagnosing lung cancer: a systematic review. Curr. Oncol. 19(1), 16–27 (2012)CrossRefGoogle Scholar
  5. 5.
    Webb, W.R., Muller, N.L., Naidich, D.P.: High–Resolution CT of the Lung. Lippincott Williams & Wilkins, Philadelphia (2001)Google Scholar
  6. 6.
    Dundar, M.M., Badve, S.: Computerized classification of intraductal breast lesions using histopathological images. IEEE Trans. Biomed. Eng. 58(7), 1977–1984 (2011)CrossRefGoogle Scholar
  7. 7.
    Sieren, J.C., Weydert, J., et al.: An automated segmentation approach for highlighting the histological complexity of human lung cancer. Ann. Biomed. Eng. 38(12), 3581–3591 (2010)CrossRefGoogle Scholar
  8. 8.
    Nguyen, K., Sabata, B., Jain, A.K.: Prostate cancer grading: gland segmentation and structural features. Pattern Recognit. Lett. 33(7), 951–961 (2012)CrossRefGoogle Scholar
  9. 9.
    Mete, M., Xu, X., et al.: Head and neck cancer detection in histopathological slides. In: 6th IEEE International Conference on Data Mining—Workshops (2006)Google Scholar
  10. 10.
    Tabesh, A., Teverovskiy, M.: Multifeature prostate cancer diagnosis and Gleason grading of histological images. IEEE Trans. Med. Imaging 26(10), 1366–1378 (2007)CrossRefGoogle Scholar
  11. 11.
    Chekkoury, A., Khurd, P., et al.: Automated malignancy detection in breast histopathological images. In: Medical Imaging 2012: Computer-Aided Diagnosis, San Diego, California, vol. 8315 (2012)Google Scholar
  12. 12.
    Jafari-Khouzani, K., Soltanian-Zadeh, H.: Multiwavelet grading of pathological images of prostate. IEEE Trans. Biomed. Eng. 50(6), 697–704 (2003)CrossRefGoogle Scholar
  13. 13.
    Khurd, P., Bahlmann, C., Gibbs-Strauss, S.: Computer-aided Gleason grading of prostate cancer histopathological images using Texton forests. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro (2010)Google Scholar
  14. 14.
    Wang, W., John, A., et al.: Detection and classification of thyroid follicular lesions based on nuclear structure from histopathology images. Cytometry Part A 77(5), 485–494 (2010)Google Scholar
  15. 15.
    Smith, J.R., Chang, S.F.: Transform features for texture classification and discrimination in large image databases. In: Proceedings of the IEEE International Conference on Image Processing (1994)Google Scholar
  16. 16.
    Scheunders, P., Livens S., et al.: Wavelet-based texture analysis. Int. J. Comput. Sci. Inf. Manag. (1997)Google Scholar
  17. 17.
    Gao, L., Li, F., Thrall, M.J.: On-the-spot lung cancer differential diagnosis by label-free, molecular vibrational imaging and knowledge-based classification. J. Biomed. Opt. 16(9), 096004 (2011). Scholar
  18. 18.
    Sambl, M.L., Camara1, F.: A novel RFE-SVM-based feature selection approach for classification. Int. J. Adv. Sci. Technol. 43, 27–36 (2012)Google Scholar
  19. 19.
    Batuwita, R., Palade, V.: Class imbalance learning methods for support vector machines. In: He, H., Ma, Y. (eds.) Imbalanced Learning: Foundations Algorithms and Applications. Wiley, New York (2013)CrossRefGoogle Scholar
  20. 20.
    Khan, A.M., Rajpoot, N.: A nonlinear mapping approach to stain normalization in digital histopathology images using image-specific color deconvolution. IEEE Trans. Biomed. Imaging 61(6), 1729–1738 (2014)CrossRefGoogle Scholar
  21. 21.
    Kumar, P.: A wavelet based methodology for scale-space anisotropic analysis. Geophys. Res. Lett. 22(20), 2777–2780 (1995)CrossRefGoogle Scholar
  22. 22.
    Do, M.N., Vetterli M.: Wavelet-based texture retrieval using generalized Gaussian density and Kullback–Leibler distance. IEEE Trans. Image Process. 11(2), 146–158 (2002)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Nadarajah, S.: A generalized normal distribution. J. Appl. Stat. 32(7), 685–694 (2005)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Johnson, D., Sinanovic, S.: Symmetrizing the Kullback-Leibler distance. IEEE Trans. Inf. Theory (2000)Google Scholar
  25. 25.
    Lin, J.: Divergence measures based on the Shannon entropy. IEEE Trans. Inf. Theory 37(1), 145–151 (1991)MathSciNetCrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Institute of Advanced Study in Science and TechnologyGuwahatiIndia

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