Texture Feature Extraction Using MGRLBP Method for Medical Image Classification

  • Suganya Ramamoorthy
  • R. Kirubakaran
  • Rajaram Siva Subramanian
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 324)

Abstract

Texture is an important significant property of medical images based on which images can be characterized and classified in a content-based image retrieval and classification system. This paper examines the feature extraction methods to ameliorate texture recognition accuracy by extracting the rotation-invariant texture feature from liver images by the individual Gabor filter method and by multi-scale Gabor rotation-invariant LBP (MGRLBP) method. The features extracted from both the approaches are tested on a set of 60 liver images of four different classes. The classification algorithms such as support vector machine (SVM) and k-nearest neighbor (KNN) were used to evaluate the extracted features from both methods, showing advancing improvements with the MGRLBP method over the individual method in the classification task.

Keywords

Texture Feature extraction Texture analysis Rotation invariant 

Notes

Acknowledgments

The authors convey their heartfelt thanks to Dr. R. Sambath, Radiologist, and Dr. P.S. Rajan, MS of GEM Hospital, Coimbatore, for providing the medical image dataset used in this paper, and also Dr. Kasthurimohan, MD of Malar Hospital, Dindigul, and Dr. Mahalakshmi, DGO of Meenakshi Mission Hospital, Madurai, for their motivation and support for conducting this work and valuable suggestions at different stages of the work.

References

  1. 1.
    I. Sluimer, A. Schilham, M. Prokop, B. van Ginneken, Computer analysis of computed tomography scans of the lung: a survey. IEEE Trans. Med. Imaging 25(4), 385–405 (2006)CrossRefGoogle Scholar
  2. 2.
    D. Mittal, V. Kumar, N. Khandelwal, N. Kalra, Neural network based focal liver lesion diagnosis using ultrasound images. Comput. Med. Imaging Graph. 35(4), 315–323 (2011)Google Scholar
  3. 3.
    L. Tesar, A. Shimizu, D. Smutek, H. Kobatake, S. Nawano, Medical image analysis of 3D CT images based on extension of Haralick texture features. Comput. Med. Imaging Graph. 32(6), 513–520 (2008)Google Scholar
  4. 4.
    V.S. Bharathi, V.S. Raghavan, L. Ganesan, Texture classification using Zernike moments, in Proceedings of 2nd FAE International Symposium (2002), pp. 292–294Google Scholar
  5. 5.
    Y. Huang, X. Han, X. Tian, Z. Zhao, J. Zhao, D. Hao, Texture analysis of ultrasonic liver images based on spatial domain methods, in 3rd International Congress on Image and Signal Processing(CISP) (2010)Google Scholar
  6. 6.
    F. Riaz, F.B. Silva, M.D. Ribeiro, M.T. Coimbra, Invariant gabor texture descriptors for classification of gastroenterology images. IEEE Trans. Biomed. Eng. 59(10), 2893–2904 (2012)Google Scholar
  7. 7.
    R. Parekh, Using texture analysis for medical diagnosis. IEEE Computer Society (2012)Google Scholar
  8. 8.
    U. Rajendra Acharya, S. Dua, X. Du, S. Vinitha Sree, C.K. Chua, Automated diagnosis of glaucoma using texture and higher order spectra features. IEEE Trans. Inf. Tech. Biomed. 15(3), 449–455 (2011)Google Scholar
  9. 9.
    Y. Song, W. Cai, Y. Zhou, D.D. Feng, Feature-based image patch approximation for lung tissue classification. IEEE Trans. Med. Imaging 32(4), 797–808 (2013)Google Scholar
  10. 10.
    O. Kayaalti, B.H. Aksebzeci, K. Deniz, M. Ozturk, S. Kara, Staging of the liver fibrosis from CT images using texture features, in International Conference on Health Informatics and Bioinformatics (HIBIT) (2012)Google Scholar
  11. 11.
    J. Virmani, V. Kumar, N. Kalra, N. Khandelwal, Prediction of cirrhosis from liver ultrasound B-mode images based on laws’ masks analysis, in International Conference on Image Information Processing (ICIIP) (2011)Google Scholar
  12. 12.
    KH. Hwang, H. Lee, D. Choi, Medical image retrieval: past and present. Healthc. Res. Inf. 18(1), 3–9 (2012)Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  • Suganya Ramamoorthy
    • 1
  • R. Kirubakaran
    • 1
  • Rajaram Siva Subramanian
    • 2
  1. 1.Department of CSEThiagarajar College of EngineeringMaduraiIndia
  2. 2.Department of ECEThiagarajar College of EngineeringMaduraiIndia

Personalised recommendations