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A Framework for Classification of Gabor Based Frequency Selective Bone Radiographs Using CNN

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Abstract

The automatic classification of bone texture into healthy or osteoporotic cases presents a major challenge since there is no visual difference between the two cases. This classification requires an inspection of the fine granularity in the bone radiographs which is usually difficult with a naked eye. We have proposed a novel method in this paper, that can be used for the classification of bone radiographs into healthy or osteoporotic cases. We mimic the observations of the physicians by preprocessing the bone radiographs with Gabor filters bearing a high frequency. Later, we design and utilize a convolutional neural network wherein filtered images are fed as input to the system which classifies the images into their respective classes. The proposed algorithm has been validated on a bone radiograph challenge dataset. Our results depict that the method proposed in this research exhibits very good results in terms of classification. A comparison of the proposed and the contemporary research methods has also been shown in this paper. The experimental results show that by exploiting high frequency Gabor filters and employing the convolutional neural network architecture, good results in performing the classification of bone radiographs are achieved.

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Notes

  1. https://www.radiologyinfo.org/en/info.cfm?pg=bonerad.

References

  1. Ajmal, H., Rehman, S., Farooq, U., U.Ain, Q., Riaz, F., Hasssan, A.: Convolutional neural network based image segmentation: a review. In: Pattern Recognition and Tracking XXIX, vol. 10649, p. 106490N. International Society for Optics and Photonics (2018)

  2. Ajmal, H., Rehman, S., Hussain, F., Abbas, M., Khan, A., Young, R., S.Alam, M.: Comparative study of local binary pattern and its shifted variant for osteoporosis identification. In: Pattern Recognition and Tracking XXIX, vol. 10649, p. 1064908. International Society for Optics and Photonics (2018)

  3. Amira, O.; Xu, S.; Du, F.; Zhang, J.; Zhang, C.; Hamza, R.: Weighted-capsule routing via a fuzzy gaussian model. Pattern Recogn. Lett. 138, 424–430 (2020)

    Article  Google Scholar 

  4. Areeckal, A.S.; Kocher, M.; et al.: Current and emerging diagnostic imaging-based techniques for assessment of osteoporosis and fracture risk. IEEE Rev. Biomed. Eng. 12, 254–268 (2018)

    Article  Google Scholar 

  5. Dong, X.; Zhou, H.; Dong, J.: Texture classification using pair-wise difference pooling-based bilinear convolutional neural networks. IEEE Trans. Image Process. 29, 8776–8790 (2020)

    Article  Google Scholar 

  6. Fan, K.C.; Hung, T.Y.: A novel local pattern descriptor-local vector pattern in high-order derivative space for face recognition. IEEE Trans. Image Process. 23(7), 2877–2891 (2014)

    Article  MathSciNet  Google Scholar 

  7. Field, D.: Relations between the statistics of natural images and the response properties of cortical cells. IEEE J. Radio Commun. Eng. 4(12), 2379 (1987)

    Google Scholar 

  8. Gabor, D.: Theory of communication. IEE J. Radio Commun. Eng. 93(26), 429 (1946)

    Google Scholar 

  9. Glaser, D.L.; Kaplan, F.S.: Osteoporosis: definition and clinical presentation. Spine 22(24), 12S–16S (1997)

    Article  Google Scholar 

  10. Granlund, D.H.: In search of a general picture processing operator. Comput. Gr. Image Processing 8, 155 (1978)

    Article  Google Scholar 

  11. Hidaka, A.; Kurita, T.: Consecutive dimensionality reduction by canonical correlation analysis for visualization of convolutional neural networks. J-Stage 2017(48), 160–167 (2017)

    Google Scholar 

  12. Hofbauer, H.; Jalilian, E.; Uhl, A.: Exploiting superior CNN-based iris segmentation for better recognition accuracy. Pattern Recogn. Lett. 120, 17–23 (2019)

    Article  Google Scholar 

  13. Houam, L., Hafiane, A., Jennane, R., Boukrouche, A., Lespessailles, E.: Trabecular bone texture classification using 1d lbp and wavelet coefficients in high-pass bands. In: International Conference on Signal, Image, Vision and their Applications SIVA, Vol. 11, pp. 21–24 (2011)

  14. Hough, S.: Fast and slow bone losers. Drugs Aging 12(1), 1–7 (1998)

    Article  Google Scholar 

  15. Jennane, R., Touvier, J., Bergounioux, M., Lespessailles, E.: A variational model for trabecular bone radiograph characterization. In: 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), pp. 1283–1286. IEEE (2014)

  16. Kavya, R.; Shivram, J.: Texture based bone-radiograph image analysis for the assessment of osteoporosis. Int. J. Eng. Res. Technol. (2015). https://doi.org/10.17577/IJERTV4IS060767

    Article  Google Scholar 

  17. Kazakia, G.J.; Majumdar, S.: New imaging technologies in the diagnosis of osteoporosis. Rev. Endocrine Metab. Disorders 7(1–2), 67–74 (2006)

    Google Scholar 

  18. Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  19. Korchiyne, R., Farssi, S.M., Sbihi, A., Touahni, R., Alaoui, M.T.: A combined method of fractal and glcm features for mri and ct scan images classification. arXiv preprint arXiv:1409.4559 (2014)

  20. Laine, A.; Fan, J.: Texture classification by wavelet packet signatures. IEEE Trans. Pattern Anal. Mach. Intell. 15(11), 1186 (2000)

    Article  Google Scholar 

  21. Le Corroller, T.; Halgrin, J.; Pithioux, M.; Guenoun, D.; Chabrand, P.; Champsaur, P.: Combination of texture analysis and bone mineral density improves the prediction of fracture load in human femurs. Osteoporos. Int. 23(1), 163–169 (2012)

    Article  Google Scholar 

  22. Lespessailles, E.; Gadois, C.; Lemineur, G.; Do-Huu, J.; Benhamou, L.: Bone texture analysis on direct digital radiographic images: precision study and relationship with bone mineral density at the os calcis. Calcif. Tissue Int. 80(2), 97–102 (2007)

    Article  Google Scholar 

  23. 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. IEEE (2014)

  24. Liu, J.; Wang, J.; Ruan, W.; Lin, C.; Chen, D.: Diagnostic and gradation model of osteoporosis based on improved deep u-net network. J. Med. Syst. 44(1), 15 (2020)

    Article  Google Scholar 

  25. Moy, J.P.: Signal-to-noise ratio and spatial resolution in x-ray electronic imagers: Is the mtf a relevant parameter? Med. Phys. 27(1), 86–93 (2000)

    Article  Google Scholar 

  26. Muller, R.; Hildebrand, T.; Ruegsegger, P.: Non-invasive bone biopsy: a new method to analyse and display the three-dimensional structure of trabecular bone. Phys. Med. Biol. 39(1), 145 (1994)

    Article  Google Scholar 

  27. Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML-10), pp. 807–814 (2010)

  28. Nemati, R.J., Rehman, S., Awan, A.B., Riaz, F.: A composite framework for segregating x-rays of osteoporotic cases from healthy controls. In: Pattern Recognition and Tracking XXIX, vol. 10649, p. 1064909. International Society for Optics and Photonics (2018)

  29. Ngo, V.Q.; Dinh, T.N.: Bone texture characterization based on contourlet and gabor tranforms. Int. J. Comput. Theory Eng. 8(2), 177 (2016)

    Article  Google Scholar 

  30. Nishiyama, K.K.; Shane, E.: Clinical imaging of bone microarchitecture with hr-pqct. Current Osteoporos. Rep. 11(2), 147–155 (2013)

    Article  Google Scholar 

  31. Ojala, T.; Pietikainen, M.; Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recogn. 29, 51 (1996)

    Article  Google Scholar 

  32. Oulhaj, H.; Rziza, M.; Amine, A.; Toumi, H.; Lespessailles, E.; El Hassouni, M.; Jennane, R.: Anisotropic discrete dual-tree wavelet transform for improved classification of trabecular bone. IEEE Trans. Med. Imaging 36(10), 2077–2086 (2017)

    Article  Google Scholar 

  33. Oulhaj, H.; Rziza, M.; Amine, A.; Toumi, H.; Lespessailles, E.; Jennane, R.; El Hassouni, M.: Trabecular bone characterization using circular parametric models. Biomed. Signal Process. Control 33, 411–421 (2017)

    Article  Google Scholar 

  34. Pan, W.; Bouslimi, D.; Karasad, M.; Cozic, M.; Coatrieux, G.: Imperceptible reversible watermarking of radiographic images based on quantum noise masking. Comput. Methods Programs Biomed. 160, 119–128 (2018)

    Article  Google Scholar 

  35. Paul, R., Alahamri, S., Malla, S., Quadri, G.J.: Make your bone great again: A study on osteoporosis classification. arXiv preprint arXiv:1707.05385 (2017)

  36. Pentland, A.A.: Fractal based description of natural scenes. IEEE Trans. Pattern Recognit. Mach. Intell. 6, 661 (1984)

    Article  Google Scholar 

  37. Pohlig, F.; Kirchhoff, C.; Lenze, U.; Schauwecker, J.; Burgkart, R.; Rechl, H.; von Eisenhart-Rothe, R.: Percutaneous core needle biopsy versus open biopsy in diagnostics of bone and soft tissue sarcoma: a retrospective study. Eur. J. Med. Res. 17(1), 29 (2012)

    Article  Google Scholar 

  38. Porter, R.; Canagarajah, N.: Robust rotation-invariant texture classification: wavelet, Gabor filter and GMRF based schemes. IEE Proc. Vis. Image Signal Process. 144(3), 180–188 (1997)

    Article  Google Scholar 

  39. Pramudito, J.; Soegijoko, S.; Mengko, T.; Muchtadi, F.; Wachjudi, R.: Trabecular pattern analysis of proximal femur radiographs for osteoporosis detection. J. Biomed. Pharm. Eng. 1(1), 45–51 (2007)

    Google Scholar 

  40. Riaz, F., Nemati, R., Ajmal, H., Hassan, A., Edifor, E., Nawaz, R.: Osteoporosis classification using texture features. In: 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS), pp. 575–579. IEEE (2019)

  41. Satpathy, A.; Jiang, X.; Eng, H.L.: Lbp-based edge-texture features for object recognition. IEEE Trans. Image Process. 23(5), 1953–1964 (2014)

    Article  MathSciNet  Google Scholar 

  42. Selvan, S.; Ramakrishnan, S.: Svd-based modeling for texture classification using wavelets transformation. IEEE Trans. Image Process. 16(11), 2688 (2007)

    Article  MathSciNet  Google Scholar 

  43. Sharp, J.T.; Wolfe, F.; Lassere, M.; Boers, M.; Van Der Heijde, D.; Larsen, A.; Paulus, H.; Rau, R.; Strand, V.: Variability of precision in scoring radiographic abnormalities in rheumatoid arthritis by experienced readers. J. Rheumatol. 31(6), 1062–1072 (2004)

    Google Scholar 

  44. Simmons, N.R.: Chest x-ray clues to osteoporosis: criteria, correlations, and consistency. Yale University School of Medicine, Technical report (2009)

  45. Singh, A.; Dutta, M.K.; Jennane, R.; Lespessailles, E.: Classification of the trabecular bone structure of osteoporotic patients using machine vision. Comput. Biol. Med. 91, 148–158 (2017)

    Article  Google Scholar 

  46. Su, B., Liu, Y., Jiang, Y., Fu, J., Quan, G.: Bone induced artifacts elimination using two-step convolutional neural network. In: 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, vol. 11072, p. 1107221. International Society for Optics and Photonics (2019)

  47. Su, R., Chen, W., Wei, L., Li, X., Jin, Q., Tao, W.: Encoded texture features to characterize bone radiograph images. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 3856–3861. IEEE (2018)

  48. Su, R.; Liu, T.; Sun, C.; Jin, Q.; Jennane, R.; Wei, L.: Fusing convolutional neural network features with hand-crafted features for osteoporosis diagnoses. Neurocomputing 385, 300 (2019)

    Article  Google Scholar 

  49. Tafraouti, A., El Hassouni, M., Toumi, H., Lespessailles, E., Jennane, R.: Osteoporosis diagnosis using fractal analysis and support vector machine. In: 2014 Tenth International Conference on Signal-Image Technology and Internet-Based Systems, pp. 73–77. IEEE (2014)

  50. Yang, M., Wang, S., Bakita, J., Vu, T., Smith, F.D., Anderson, J.H., Frahm, J.M.: Re-thinking cnn frameworks for time-sensitive autonomous-driving applications: Addressing an industrial challenge. In: 2019 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), pp. 305–317. IEEE (2019)

  51. Yger, F.: Challenge ieee-isbi/tcb: application of covariance matrices and wavelet marginals. arXiv preprint arXiv:1410.2663 (2014)

  52. Zhao, Y.; Huang, D.S.; Jia, W.: Completed local binary count for rotation invariant texture classification. IEEE Trans. Image Process. 21(10), 4492–4497 (2012)

    Article  MathSciNet  Google Scholar 

  53. Zheng, K., Makrogiannis, S.: Bone texture characterization for osteoporosis diagnosis using digital radiography. In: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1034–1037. IEEE (2016)

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Authors and Affiliations

  1. National University of Sciences and Technology (NUST), Islamabad, Pakistan

    Rehan J. Nemati, Farhan Riaz, Ali Hassan, Muhammad Abbas, Farhan Hussain & Saddaf Rubab

  2. University of Derby, Derby, United Kingdom

    Muhammad Ajmal Azad

  3. HITEC University, Taxila, Pakistan

    Saad Rehman

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  1. Rehan J. Nemati
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Correspondence to Rehan J. Nemati.

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Nemati, R.J., Riaz, F., Hassan, A. et al. A Framework for Classification of Gabor Based Frequency Selective Bone Radiographs Using CNN. Arab J Sci Eng 46, 4141–4152 (2021). https://doi.org/10.1007/s13369-021-05339-7

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