Advertisement

Optimized Gabor Feature Extraction for Mass Classification Using Cuckoo Search for Big Data E-Healthcare

  • Salabat Khan
  • Amir Khan
  • Muazzam Maqsood
  • Farhan Aadil
  • Mustansar Ali Ghazanfar
Article
  • 9 Downloads

Abstract

Widespread use of electronic health records is a major cause of a massive dataset that ultimately results in Big Data. Computer-aided systems for healthcare can be an effective tool to automatically process such big data. Breast cancer is one of the major causes of high mortality rate among women in the world since it is difficult to detect due to lack of early symptoms. There is a number of techniques and advanced technologies available to detect breast tumors nowadays. One of the common approaches for breast tumour detection is mammography. The similarity between the normal (unaffected) tissues and the masses (affected) tissues is often very high that leads to false positives (FP). In the field of medicine, the sensitivity to false positives is very high because it results in false diagnosis and can lead to serious consequences. Therefore, it is a challenge for the researchers to correctly distinguish between the normal and affected tissues to increase the detection accuracy. Radiologists use Gabor filter bank for feature extraction and apply it to the entire input image that yields poor results. The proposed system optimizes the Gabor filter bank to select most appropriate Gabor filter using a metaheuristic algorithm known as “Cuckoo Search”. The proposed algorithm is run over sub-images in order to extract more descriptive features. Moreover, feature subset selection is used to reduce feature size because feature extracted from the segmented region of interest will be high dimensional and cannot be handled easily. This algorithm is more efficient, fast, and less complex and spawns improved results. The proposed method is tested on 2000 mammograms taken from DDSM database and outperforms some of the best techniques used for mammogram classification based on Sensitivity, Specificity, Accuracy, and Area under the curve (ROC).

Keywords

Mammography Gabor filters Optimization Cuckoo search 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Guo, R., et al.: Ultrasound imaging technologies for breast cancer detection and management: A review. Ultrasound Med. Biol. 44.1, 37–70 (2018)CrossRefGoogle Scholar
  2. 2.
    Houssami, N., et al.: Breast cancer detection using single-reading of breast tomosynthesis (3D-mammography) compared to double-reading of 2D-mammography: Evidence from a population-based trial. Cancer Epidemiol. 47, 94–99 (2017)CrossRefGoogle Scholar
  3. 3.
    Altekruse, S.F., Kosary, C.L., Krapcho, M., et al.: SEER Cancer Statistics Review, vol. 1975-2007. National Cancer Institute, Bethesda (2010)Google Scholar
  4. 4.
    Zheng, Y.: Breast cancer detection with gabor features from digital mammograms. Algorithms 3.1, 44–62 (2010)CrossRefGoogle Scholar
  5. 5.
    Eltoukhy, M.M., Faye, I., Samir, B.B.: Breast cancer diagnosis in digital mammogram using multiscale curvelet transform. Comput. Med. Imag. Graph 34.4, 269–276 (2010)CrossRefGoogle Scholar
  6. 6.
    Llad, X., Oliver, A., Freixenet, J., Mart, R., Mart, J.: A textural approach for mass false positive reduction in mammography. Comput. Med. Imaging Graph. 33(6), 415–422 (2009)CrossRefGoogle Scholar
  7. 7.
    Hussain, M., et al.: Effective extraction of Gabor features for false positive reduction and mass classification in mammography. Appl. Math. 8.1L, 397–412 (2014)Google Scholar
  8. 8.
    Sun, Z., Bebis, G., Miller, R.: Monocular precrash vehicle detection: features and classifiers. IEEE Trans. Image Process 15.7, 2019–2034 (2006)Google Scholar
  9. 9.
    Yang, X.-S., Deb, S.: Cuckoo search via Lévy flights. World Congress on Nature & Biologically Inspired Computing, 2009. NaBIC 2009. IEEE (2009)Google Scholar
  10. 10.
    Jaganathan, Y., Ila, V.: An integrated framework based on texture features cuckoo search and relevance vector machine for medical image retrieval system. Am. J. Appl. Sci. 10, 11 (2013)Google Scholar
  11. 11.
    Oliver, A., et al.: A review of automatic mass detection and segmentation in mammographic images. Med. Image Anal. 14.2, 87–110 (2010)CrossRefGoogle Scholar
  12. 12.
    Domínguez, R.A., Nandi, A.K.: Toward breast cancer diagnosis based on automated segmentation of masses in mammograms. Pattern Recogn. 42.6, 1138–1148 (2009)CrossRefGoogle Scholar
  13. 13.
    Tang, J., et al.: Computer-aided detection and diagnosis of breast cancer with mammography: Recent advances. IEEE Trans. Inform. Technol. Biomed. 13.2, 236–251 (2009)CrossRefGoogle Scholar
  14. 14.
    Elter, M., Horsch, A.: CADx of mammographic masses and clustered microcalcifications: A review. Med. Phys. 36.6, 2052–2068 (2009)CrossRefGoogle Scholar
  15. 15.
    Borges Sampaio, W., et al.: Detection of masses in mammogram images using CNN, geostatistic functions and SVM. Comput. Biol. Med. 41.8, 653–664 (2011)CrossRefGoogle Scholar
  16. 16.
    Wei, D., et al.: Classification of mass and normal breast tissue on digital mammograms: Multiresolution texture analysis. Med. Phys. 22.9, 1501–1513 (1995)CrossRefGoogle Scholar
  17. 17.
    Turner, M.R.: Texture discrimination by Gabor functions. Biol. Cybern. 55.2-3, 71–82 (1986)Google Scholar
  18. 18.
    Bhangale, T., Desai, U.B., Sharma, U.: An unsupervised scheme for detection of microcalcifications on mammograms. In: 2000 Proceedings International Conference on Image Processing, vol. 1. IEEE (2000)Google Scholar
  19. 19.
    Rogova, G.L., Stomper, P.C., Ke, C.-C.: Microcalcification texture analysis in a hybrid system for computer-aided mammography. Medical Imaging’99 International Society for Optics and Photonics (1999)Google Scholar
  20. 20.
    Cawley, G.C., Talbot, N.L.C., Girolami, M.: Sparse multinomial logistic regression via bayesian l1 regularisation. Adv. Neural Inf. Process. Syst. 19, 209 (2007)Google Scholar
  21. 21.
    Buciu, I., Gacsadi, A.: Directional features for automatic tumor classification of mammogram images. Biomed. Signal Process. Control 6.4, 370–378 (2011)CrossRefGoogle Scholar
  22. 22.
    Gupta, N., Ujjwal, R.L.: An efficient incremental clustering algorithm. World Comput. Sci. Inf. Technol. J. 3, 5 (2013)Google Scholar
  23. 23.
    Wernick, M.N., et al.: Machine learning in medical imaging. Signal Process. Mag. IEEE 27.4, 25–38 (2010)CrossRefGoogle Scholar
  24. 24.
    Madigan, D., et al.: Bayesian multinomial logistic regression for author identification. Bayesian Inference Maximum Entropy Methods Sci. Eng. 803, 509–516 (2005)CrossRefGoogle Scholar
  25. 25.
    Krishnapuram, B., et al.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE Trans. Pattern Anal. Mach. Intell. 27.6, 957–968 (2005)CrossRefGoogle Scholar
  26. 26.
    Hsu, C.-W., Chang, C.-C., Lin, C.-J.: A practical guide to support vector classification (2003)Google Scholar
  27. 27.
    Ganesan, K., et al.: One-class classification of mammograms using trace transform functionals. IEEE Trans. Instrum. Measur. 63.2, 304–311 (2014)CrossRefGoogle Scholar
  28. 28.
    Dheeba, J., Singh, N.A., Tamil Selvi, S.: Computer-aided detection of breast cancer on mammograms: A swarm intelligence optimized wavelet neural network approach. J. Biomed. Inf. 49, 45–52 (2014)CrossRefGoogle Scholar
  29. 29.
    Mohamed, H., Mabrouk, M.S., Sharawy, A.: Computer aided detection system for micro calcifications in digital mammograms. Comput. Methods Programs Biomed. 116.3, 226–235 (2014)CrossRefGoogle Scholar
  30. 30.
    Jen, C.-C., Yu, S.-S.: Automatic detection of abnormal mammograms in mammographic images. Expert Syst. Appl. 42.6, 3048–3055 (2015)CrossRefGoogle Scholar
  31. 31.
    Beura, S., Majhi, B., Dash, R.: Mammogram classification using two dimensional discrete wavelet transform and gray-level co-occurrence matrix for detection of breast cancer. Neurocomputing 154, 1–14 (2015)CrossRefGoogle Scholar
  32. 32.
    Acharya, R., et al.: Computer-based identification of breast cancer using digitized mammograms. J. Med. Syst. 32.6, 499–507 (2008)CrossRefGoogle Scholar
  33. 33.
    Raghavendra, U., et al.: Application of Gabor wavelet and locality sensitive discriminant analysis for automated identification of breast cancer using digitized mammogram images. Appl. Soft Comput. 46, 151–161 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  • Salabat Khan
    • 1
  • Amir Khan
    • 1
  • Muazzam Maqsood
    • 1
  • Farhan Aadil
    • 1
  • Mustansar Ali Ghazanfar
    • 2
  1. 1.COMSATS University IslamabadAttock CampusPakistan
  2. 2.Department of Software EngineeringUniversity of Engineering and TechnologyTaxilaPakistan

Personalised recommendations