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Medical Image Classification with Hand-Designed or Machine-Designed Texture Descriptors: A Performance Evaluation

  • Joke A. Badejo
  • Emmanuel Adetiba
  • Adekunle Akinrinmade
  • Matthew B. Akanle
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10814)

Abstract

Accurate diagnosis and early detection of various disease conditions are key to improving living conditions in any community. The existing framework for medical image classification depends largely on advanced digital image processing and machine (deep) learning techniques for significant improvement. In this paper, the performance of traditional hand-designed texture descriptors within the image-based learning paradigm is evaluated in comparison with machine-designed descriptors (extracted from pre-trained Convolution Neural Networks). Performance is evaluated, with respect to speed, accuracy and storage requirements, based on four popular medical image datasets. The experiments reveal an increased accuracy with machine-designed descriptors in most cases, though at a higher computational cost. It is therefore necessary to consider other parameters for tradeoff depending on the application being considered.

Keywords

Medical image classification Deep learning Convolution Neural Network Texture descriptors 
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Notes

Acknowledgements

This research was sponsored under the Centre for Research Innovation and Development Research Grant of Covenant University. The authors who shared their MATLAB code and toolboxes for LBP, LTP, LPQ, CLBP, RICLBP and MatConvNet are appreciated.

References

  1. 1.
    Nanni, L., Lumini, A., Brahnam, S.: Local binary patterns variants as texture descriptors for medical image analysis. Artif. Intell. Med. 49(2), 117–125 (2010)CrossRefGoogle Scholar
  2. 2.
    Lumini, A., Nanni, L., Brahnam, S.: Multilayer descriptors for medical image classification. Comput. Biol. Med. 72, 239–247 (2016)CrossRefGoogle Scholar
  3. 3.
    Verma, B., McLeod, P., Klevansky, A.: Classification of benign and malignant patterns in digital mammograms for the diagnosis of breast cancer. Expert Syst. Appl. 37(4), 3344–3351 (2010)CrossRefGoogle Scholar
  4. 4.
    Moura, D.C., López, M.A.G.: An evaluation of image descriptors combined with clinical data for breast cancer diagnosis. Int. J. Comput. Assist. Radiol. Surg. 8(4), 561–574 (2013)CrossRefGoogle Scholar
  5. 5.
    Liu, D., Wang, S., Huang, D., Deng, G., Zeng, F., Chen, H.: Medical image classification using spatial adjacent histogram based on adaptive local binary patterns. Comput. Biol. Med. 72, 185–200 (2016)CrossRefGoogle Scholar
  6. 6.
    Kopans, D.B.: The positive predictive value of mammography. Am. J. Roentgenol. 158, 521–526 (1992)CrossRefGoogle Scholar
  7. 7.
    Knutzen, A.M., Gisvold, J.J.: Likelihood of malignant disease for various categories of mammographically detected, nonpalpable breast lesions. In: Mayo Clinic Proceedings (1993)Google Scholar
  8. 8.
    Jiang, Y., et al.: Malignant and benign clustered microcalcifications: automated feature analysis and classification. Radiology 198, 671–678 (1996)CrossRefGoogle Scholar
  9. 9.
    Hobson, P., Lovell, B.C., Percannella, G., Saggese, A., Vento, M., Wiliem, A.: HEp-2 staining pattern recognition at cell and specimen levels: datasets, algorithms and results. Pattern Recognit. Lett. 82, 12–22 (2016)CrossRefGoogle Scholar
  10. 10.
    Rubin, G.D.: Data explosion: the challenge of multidetector-row CT. Eur. J. Radiol. 2, 74–80 (2000)CrossRefGoogle Scholar
  11. 11.
    Shen, W., Zhou, M., Yang, F., Yang, C., Tian, J.: Multi-scale convolutional neural networks for lung nodule classification. In: Ourselin, S., Alexander, D.C., Westin, C.-F., Cardoso, M.J. (eds.) IPMI 2015. LNCS, vol. 9123, pp. 588–599. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-19992-4_46CrossRefGoogle Scholar
  12. 12.
    Tajbakhsh, N., et al.: Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans. Med. Imaging 35(5), 1299–1312 (2016)CrossRefGoogle Scholar
  13. 13.
    Li, Q., Cai, W., Wang, X., Zhou, Y., Feng, D.D., Chen, M.: Medical image classification with convolutional neural network. In: 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV), pp. 844–848 (2014)Google Scholar
  14. 14.
    Shin, H.C., et al.: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Spanhol, F.A., Oliveira, L.S., Petitjean, C., Heutte, L.: A dataset for breast cancer histopathological image classification. IEEE Trans. Biomed. Eng. 63, 1455–1462 (2016)CrossRefGoogle Scholar
  16. 16.
    Gao, Z., Wang, L., Zhou, L., Zhang, J.: HEp-2 cell image classification with deep convolutional neural networks. IEEE J. Biomed. Heal. Inform. 21(2), 416–428 (2017)CrossRefGoogle Scholar
  17. 17.
    Bello-Cerezo, R., Bianconi, F., Cascianelli, S., Fravolini, M.L., di Maria, F., Smeraldi, F.: Hand-designed local image descriptors vs. off-the-shelf CNN-based features for texture classification: an experimental comparison. In: De Pietro, G., Gallo, L., Howlett, R.J., Jain, L.C. (eds.) KES-IIMSS 2017. SIST, vol. 76, pp. 1–10. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-59480-4_1CrossRefGoogle Scholar
  18. 18.
    Liu, L., Fieguth, P., Guo, Y., Wang, X., Pietikäinen, M.: Local binary features for texture classification: taxonomy and experimental study. Pattern Recognit. 62, 135–160 (2017)CrossRefGoogle Scholar
  19. 19.
    Hertel, L., Barth, E., Kaster, T., Martinetz, T.: Deep convolutional neural networks as generic feature extractors. In: Proceedings of the International Joint Conference on Neural Networks, vol. 2015, September (2015)Google Scholar
  20. 20.
    Chebira, A., et al.: A multiresolution approach to automated classification of protein subcellular location images. BMC Bioinform. 8, 210 (2007)CrossRefGoogle Scholar
  21. 21.
    Jantzen, J., Norup, J., Dounias, G., Bjerregaard, B.: Pap-smear benchmark data for pattern classification. In: Proceedings of NiSIS 2005 Nature Inspired Smart Information System, pp. 1–9 (2005)Google Scholar
  22. 22.
    Boland, M.V., Murphy, R.F.: A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells. Bioinformatics 17(12), 1213–1223 (2001)CrossRefGoogle Scholar
  23. 23.
    Nanni, L., Ghidoni, S., Brahnam, S.: Handcrafted vs. non-handcrafted features for computer vision classification. Pattern Recognit. 71, 158–172 (2017)CrossRefGoogle Scholar
  24. 24.
    Liu, L., Chen, J., Fieguth, P., Zhao, G., Chellappa, R., Pietikainen, M.: A survey of recent advances in texture representation. arXiv Preprint arXiv:1801.10324 (2018)
  25. 25.
    Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)CrossRefzbMATHGoogle Scholar
  26. 26.
    Krizhevsky, A., Sulskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems vol. 60, no. 6, pp. 84–90 (2012)Google Scholar
  27. 27.
    Vedaldi, A., Lenc, K.: Convolutional neural networks for MATLAB (2014)Google Scholar
  28. 28.
    Adetiba, E., Olugbara, O.O.: Lung cancer prediction using neural network ensemble with histogram of oriented gradient genomic features. Sci. World J. 2015, 17p (2015)Google Scholar
  29. 29.
    Adetiba, E., Olugbara, O.O.: Improved classification of lung cancer using radial basis function neural network with affine transforms of voss representation. PLoS ONE 10(12), e0143542 (2015)CrossRefGoogle Scholar
  30. 30.
    Cimpoi, M., Maji, S., Kokkinos, I., Vedaldi, A.: Deep filter banks for texture recognition, description, and segmentation. Int. J. Comput. Vis. 118(1), 65–94 (2016)MathSciNetCrossRefGoogle Scholar
  31. 31.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks, pp. 1097–1105 (2012)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Joke A. Badejo
    • 1
  • Emmanuel Adetiba
    • 1
    • 2
    • 3
  • Adekunle Akinrinmade
    • 1
  • Matthew B. Akanle
    • 1
    • 2
  1. 1.Department of Electrical and Information EngineeringCovenant UniversityOtaNigeria
  2. 2.Center for Systems and Information Services (CSIS)Covenant UniversityOtaNigeria
  3. 3.HRA, Institute for Systems ScienceDurban University of TechnologyDurbanSouth Africa

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