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Multi-Scale Directional Mask Pattern for Medical Image Classification and Retrieval

  • Akshay A. Dudhane
  • Sanjay N. Talbar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 703)

Abstract

This paper presents a classification scheme for interstitial lung disease (ILD) pattern using patch-based approach and artificial neural network (ANN) classifier. A new feature descriptor, Multi-Scale Directional Mask Pattern (MSDMP), is proposed for feature extraction. Proposed MSDMP extracts microstructure information from a (31 × 31) size patches of the region of interest (ROI) which were marked by the radiologists. A two-layer feed-forward neural network is used for classification of ILD patterns. Also, proposed MSDMP feature descriptor has been tested on medical image retrieval system to check its robustness. Two benchmark medical datasets are used to evaluate the proposed descriptor. Performance analysis shows that the proposed feature descriptor outperforms the other existing state-of-the-art methods in terms of average recognition rate (ARR) and F-score.

Keyword

ILD artificial neural network feature descriptor 

References

  1. 1.
    Lung Disease & Respiratory Health Center. http://www.webmd.com/lung.
  2. 2.
    A. H. Mir, M. Hanmandlu, and S. N. Tandon(1995) Texture analysis of CT images. Eng. Med. Biol. Mag. IEEE, vol. 14, no. 6, pp. 781–786.CrossRefGoogle Scholar
  3. 3.
    R. Uppaluri, T. Mitsa, M. Sonka, E. A. Hoffman, and G. McLennan(1997) Quantification of pulmonary emphysema from lung computed tomography images. American journal of respiratory and critical care medicine, vol. 156, no. 1, pp. 248–254.CrossRefGoogle Scholar
  4. 4.
    Y. Xu, M. Sonka, G. McLennan, J. Guo, and E. A. Huffman (2006) MDCT-based 3-D texture classification of emphysema and early smoking related lung pathologies. IEEE transactions on medical imaging, vol. 25, no. 4, pp. 464–475.CrossRefGoogle Scholar
  5. 5.
    I. C. Sluimer, P. F. van Waes, M. A. Viergever, and B. van Ginneken(2003) Computer-aided diagnosis in high resolution CT of the lungs. Medical physics, vol. 30, no. 12, pp. 3081–90.CrossRefGoogle Scholar
  6. 6.
    Y. Uchiyama, S. Katsuragawa, H. Abe, J. Shiraishi, F. Li, Q. Li, C.-T. Zhang, K. Suzuki, and K. Doi (2003) Quantitative computerized analysis of diffuse lung disease in high-resolution computed tomography. Medical Physics, vol. 30, no. 9, pp. 2440–54.CrossRefGoogle Scholar
  7. 7.
    A. Depeursinge, D. Sage, A. Hidki, A. Platon, P.-A. Poletti, M. Unser, and H. Müller (2007) Lung tissue classification using wavelet frames. In: 29th Annual International Conference of the IEEE. EMBS 2007, pp. 6259–62.Google Scholar
  8. 8.
    A. Depeursinge, D. Ville, P. A., A. Geissbuhler, P. Poletti, and H. Muller (2012) Near-Affine-Invariant Texture Learning for Lung Tissue Analysis Using IsotropicWavelet Frames. IEEE Transactions on Information Technology in Biomedicine, vol. 16, no. 4, pp. 665–675.Google Scholar
  9. 9.
    A. Depeursinge, P. Pad, A. S. Chin, A. N. Leung, D. L. Rubin, H. Muller, and M. Unser (2015) Optimized steerable wavelets for texture analysis of lung tissue in 3-D CT: Classification of usual interstitial pneumonia. In: 12th International Symposium on Biomedical Imaging (ISBI) 2015, pp. 403–6.Google Scholar
  10. 10.
    T. Ojala, M. Pietikäinen, and T. Mäenpää (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary pattern. IEEE Transactions on pattern analysis and machine intelligence, vol. 24, no. 7, pp. 971–87.CrossRefGoogle Scholar
  11. 11.
    S. Murala, R. P. Maheshwari, and R. Balasubramanian (2012) Local Tetra Patterns : A New Feature Descriptor for Content-Based Image Retrieval. IEEE Transactions on Image Processing, vol. 21, no. 5, pp. 2874–86.MathSciNetCrossRefGoogle Scholar
  12. 12.
    S. Murala and Q. M. J. Wu (2015) Spherical symmetric 3D local ternary patterns for natural, texture and biomedical image indexing and retrieval. Neurocomputing, vol. 149, pp. 1502–14.CrossRefGoogle Scholar
  13. 13.
    B. Manjunath and W. Ma (1996) Texture features for browsing and retrivieval of image data. IEEE Transactions on pattern analysis and machine intelligence, vol. 18, no. 8, pp. 837–42.CrossRefGoogle Scholar
  14. 14.
    A Dudhane, G Shingadkar, P Sanghavi, B Jankharia and S Talbar (2017) Interstitial Lung Disease Classification Using Feed Forward Neural Networks. In: ICCASP, Advances in Intelligent Systems Research, vol. 137, pp. 515–521.Google Scholar
  15. 15.
    J. Han and K. K. Ma (2007) Rotation-invariant and scale-invariant Gabor features for texture image retrieval. Image and vision computing, vol. 25, no. 9, pp. 1474–81.CrossRefGoogle Scholar
  16. 16.
    S. N. Talbar, R. S. Holambe, and T. R. Sontakke (1998) Supervised texture classification using wavelet transform. In 4th international conference on Signal Processing Proceedings ICSP ’98, pp. 1177–80.Google Scholar
  17. 17.
    M. Nagao, K. Murase, Y. Yasuhara, and I. Junpei (1998) Quantitative Analysis of Pulmonary Emphysema : Three-DimensionalFractal Analysis of Single-Photon Emission ComputedTomography ImagesObtained with a Carbon ParticleRadioaerosol. American journal of roentgenology, vol. 171, no. 6, pp. 1657–63.CrossRefGoogle Scholar
  18. 18.
    Y. Song, W. Cai, Y. Zhou, and D. D. Feng (2013) Feature-based image patch approximation for lung tissue classification. IEEE transactions on medical imaging, vol. 32, no. 4, pp. 797–808.CrossRefGoogle Scholar
  19. 19.
    T. Ishida, S. Katsuragawa, K. Ashizawa, H. MacMahon, and K. Doi (1998) Application of artificial neural networks for quantitative analysis of image data in chest radiographs for detection of interstitial lung disease. Journal of digital imaging, vol. 11, no. 4, pp. 182–192.CrossRefGoogle Scholar
  20. 20.
    K. Ashizawa, T. Ishida, H. MacMahon, C. J. Vyborny, S. Katsuragawa, and K. Doi (1999) Artificial neural networks in chest radiography: Application to the differential diagnosis of interstitial lung disease. Academic radiology, vol. 6, no. 1, pp. 2–9.CrossRefGoogle Scholar
  21. 21.
    M. Anthimopoulos, S. Christodoulidis, L. Ebner, A. Christe, and S. Mougiakaou (2016) Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network. IEEE transactions on medical imaging, vol. 35, no. 5, pp. 1207–1216.CrossRefGoogle Scholar
  22. 22.
    Y. Song, W. Cai, H. Huang, Y. Zhou, D. D. Feng, Y. Wang, M. J. Fulham, and M. Chen (2015) Large margin local estimate with applications to medical image classification. IEEE transactions on medical imaging, vol. 34, no. 6, pp. 1362–1377.CrossRefGoogle Scholar
  23. 23.
    L. Böröczky, L. Zhao, and K. P. Lee (2006) Feature subset selection for improving the performance of false positive reduction in lung nodule CAD. IEEE Transactions on Information Technology in Biomedicine, vol. 10, no. 3, pp. 504–511.CrossRefGoogle Scholar
  24. 24.
    M. M. Galloway (1975) Texture analysis using gray level run lengths. Computer graphics and image processing, vol. 4, no. 2, pp. 172–179.CrossRefGoogle Scholar
  25. 25.
    X. Tang (2002) Texture information in run-length matrices. IEEE transactions on image processing, vol. 7, no. 11, pp. 1602–1609.CrossRefGoogle Scholar
  26. 26.
    O. Friman, U. Tylén, H. Knutsson, M. Borga, and M. Lundberg (2002) Recognizing emphysema - a neural network approach. In: 16th International Conference on Pattern Recognition, vol. 1, pp. 512–515.Google Scholar
  27. 27.
    A. Depeursinge, A. Vargas, A. Platon, A. Geissbuhler, P. A. Poletti, and H. Müller (2012) Building a reference multimedia database for interstitial lung diseases. Computerized medical imaging and graphics, vol. 36, no. 3, pp. 227–238.CrossRefGoogle Scholar
  28. 28.
    VIA/ELCAP CT Lung Image Dataset, available from [online]: https://veet.via.cornell.edu/lungdb.html.

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Electronics & Telecommunication EngineeringSGGSIE&TNandedIndia

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