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Modality Classification Using Texture Features

Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 150)

Abstract

Medical image classification based on the image modality is one of the most important and crucial tasks in the medical image analysis. Due to its importance, the aim of the paper is to investigate modality medical image classification problem by using a combination of several classification techniques and feature extraction algorithms over a set of medical images. Four feature extraction methods were used in this paper: LBP, GLDM, GLRLM, Haralick texture features. Additionally we concatenated all four features in one single feature to assess their joint performance. The feature extraction algorithms are tested over three classifiers: SVM extended for multiclass classification based on one against all strategy, k-nearest neighbor and C4.5 algorithm. This examination was conducted over a set of medical images provided by ImageCLEF. The dataset contains 18 classes of images, with a total of 988 images. The distribution of number of images per class is not uniform, so this additionally burdens the task. The best results were provided when the images were described with concatenated descriptors and classified with SVM classifier, with a classification accuracy of 73%.

Keywords

Medical images modality based classification feature extraction LBP GLDM GLRLM Haralick SVM k-nearest neighbor C4.5 

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References

  1. 1.
    Müller, H., Michoux, N., Bandon, D., Geissbuhler, A.: A review of content-based image retrieval systems in medical applications – clinical benefits and future directions. International Journal of Medical Informatics 73, 1–23 (2004)CrossRefGoogle Scholar
  2. 2.
    Kalpathy-Cramer, J., Hersh, W.R.: Automatic Image Modality Based Classification and Annotation to Improve Medical Image Retrieval. In: MedInfo, pp. 1334–1338 (2007)Google Scholar
  3. 3.
    Florea, F., Müller, H., Rogozan, A., Geissbühler, A., Darmoni, S.: Medical image categorization with MedIC andMedGIFT. In: Medical Informatics Europe (2006)Google Scholar
  4. 4.
    Kalpathy-Cramer, J., Hersh, W.: Multimodal medical image retrieval: image categorization to improve search precision. In: MIR 2010: Proceedings of the International Conference on Multimedia Information Retrieval, pp. 165–174. ACM, New York (2010)CrossRefGoogle Scholar
  5. 5.
    Perronnin, F., Sanchez, J., Liu, Y.: Large-scale image categorization with explicit data embedding. In: CVPR (2010)Google Scholar
  6. 6.
    Perronnin, F., Liu, Y., Sanchez, J., Poirier, H.: Large-scale image retrieval with compressed fisher vectors. In: CVPR (2010)Google Scholar
  7. 7.
    Perronnin, F., Dance, C.: Fisher kernels on visual vocabularies for image categorization. In: CVPR (2007)Google Scholar
  8. 8.
    Muller, H., Kalpathy-Cramer, J., Eggel, I., Bedrick, S.: Overview of the clef 2010 medical image retrieval track. In: Working Notes of CLEF 2010, Padova, Italy (2010)Google Scholar
  9. 9.
    Wu, H., Hu, C., Chen, S.: UESTC at Image. CLEF 2010 Medical Retrieval Task (2010)Google Scholar
  10. 10.
    Kalpathy-Cramer, J., Hersh, W.: Automatic image modality based classification and annotation to improve medical image retrieval. Studies in Health Technology and Informatics 129(2), 1334 (2007)Google Scholar
  11. 11.
    Christopher Burges, J.C.: A Tutorial on Support Vector Machines for Pattern Recogni-tion. Data Mining and Knowledge Discovery 2, 121–167 (1998)CrossRefGoogle Scholar
  12. 12.
    Wareld, S.K., Kaus, M., Jolesz, F.A., Kikinis, R.: Adaptive, template moderated, spatially varying statistical classification. Med. Image Anal. 4(1), 43–55 (2000)CrossRefGoogle Scholar
  13. 13.
    Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Fifth Annual Workshop on Computational Learning Theory, pp. 144–152. ACM, Pittsburgh (1992)CrossRefGoogle Scholar
  14. 14.
    Kotsiantis, S.B.: Supervised Machine Learning: A Review of Classification Techniques. Informatica 31, 249–268 (2007)MathSciNetzbMATHGoogle Scholar
  15. 15.
    Vapnik, V.: The Nature of Statistical Learning Theory, 2nd edn. Springer, New York (1999)zbMATHGoogle Scholar
  16. 16.
    Burges, C.J.C.: A tutorial on support vector machine for pattern recognition. Data Min. Knowl. Disc. 2, 121 (1998)CrossRefGoogle Scholar
  17. 17.
    Joachims, T.: Making large-scale SVM learning practical. In: Schölkopf, B., Burges, C.J.C., Smola, A.J. (eds.) Advances in Kernel Methods—Support Vector Learning, pp. 169–184. MIT Press, Cambridge (1999)Google Scholar
  18. 18.
    Wu, X., Kumar, V., Quinlan, J.R., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G., Liu, B., Yu, P., Zhou, Z., Steinbach, M., Hand, D., Steinberg, D.: Top 10 algorithms in data mining. Knowledge and Information Systems 14(1), 1–37 (2008)CrossRefGoogle Scholar
  19. 19.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers (1993)Google Scholar
  20. 20.
    Quinlan, J.R.: Improved use of continuous attributes in c4.5. Journal of Artificial Intelligence Research 4, 77–90 (1996)zbMATHGoogle Scholar
  21. 21.
    Quinlan, J.R.: Induction of decision trees. Machine learning 1(1), 81–106 (1986)Google Scholar
  22. 22.
    Ojala, T., Pietikainen, M., Harwood, D.: A Comparative Study of Texture Measures with Classification Based on Feature Distributions. Pattern Recognition 29(1), 51–59 (1996)CrossRefGoogle Scholar
  23. 23.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Trans. Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)CrossRefzbMATHGoogle Scholar
  24. 24.
    Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, 610–621 (1973)Google Scholar
  25. 25.
    Weszaka, J.S., Dyer, C.R., Rosenfeld, A.: A comperative study of texture measures for terrain classification. IEEE Trans. on Syst., Man, Cyber., 269–285 (1976)Google Scholar
  26. 26.
    Conners, R.W., Harlow, C.A.: A theoretical comparison of texture algorithms (1980)Google Scholar
  27. 27.
    Galloway, M.M.: Texture analysis using gray level run lengths, Comput. Graphics Image Processing 4, 172–179 (1975)CrossRefGoogle Scholar
  28. 28.
    Muller, H., Kalpathy-Cramer, J., Eggel, I., Bedrick, S., Kahn Jr., C. E., Hersh, W.: Overview of the CLEF 2010 medical image retrieval track. In: The Working Notes of CLEF 2010 (2010)Google Scholar
  29. 29.
    Joachims, T.: Making large-scale SVM learning practical. In: Advances in Kernel Methods, pp. 169–184. MIT Press, Cambridge (1999)Google Scholar
  30. 30.
    Witten, I., Frank, E.: Data Mining: Practical machine learning tools and techniques, San Francisco (2005)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Faculty of Electrical Engineering and Information TechnologiesSs. Cyril and Methodious UniversitySkopjeMacedonia

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