Advertisement

A Discrete Level Set Approach for Texture Analysis of Microscopic Liver Images

  • Daniela IacovielloEmail author
Chapter
Part of the Computational Methods in Applied Sciences book series (COMPUTMETHODS, volume 19)

Abstract

In this paper the analysis of microscopic liver tissue images is addressed to identify abnormal zones due to the presence of tissue with necrosis, or to malignant lymphoma; the study is performed by texture analysis. A discrete level set approach is considered, applying the well know segmentation algorithm to a new data constituted by a linear combination of the matrices of Uniformity, Contrast and Entropy. The proposed method makes use of the classification capability of the discrete level set analysis applied to a suitable transformation of the original data. The algorithm is applied to a significant set of liver tissue, showing encouraging results.

Keywords

Texture analysis Level set Microscopic liver images 

References

  1. 1.
    Ahmadian, A., Mostafa, A., Abolhassani, M.D., Salimpou, Y.: A texture classification method for diffused liver diseases using Gabor wavelets. In: Proceedings of the IEEE Engineering in Medicine and Biology 27th Annual Conference, pp.1567–1570, Shanghai, China(2005)Google Scholar
  2. 2.
    Balasubramanian, D., Srinivasan, P., Gurrupatham, R.: Automatic classification of focal lesions in ultrasound liver images using principal component analysis and neural networks. In: Proceedings of the 29th Annual International Conference of the IEEE EMBS, pp.2134–2137, Lyon(2007)Google Scholar
  3. 3.
    Besson, S.J., Barlaud, M., Aubert, G.: Image segmentation using active contours: calculus of variations for shape gradients? SIAM J. Appl. Math. 63, 2128–2154(2003)zbMATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    De Santis, A., Iacoviello, D.: A discrete level set approach for image segmentation. Signal Image Video Process. 1, 303–320(2007)zbMATHCrossRefGoogle Scholar
  5. 5.
    De Santis, A., Iacoviello, D.: Robust real time eye tracking for computer interface for disabled people. Comput. Methods Programs Biomed. 96, 1–11(2009)CrossRefGoogle Scholar
  6. 6.
    Gonzales, R.C., Woods, R.E.: Digital Image Processing. Prentice hall Inc, NJ. SMC-3 6, 610–621 (2002)Google Scholar
  7. 7.
    Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. SMC-3 6, 610–621(1973)Google Scholar
  8. 8.
    Horng, M.H.: An ultrasonic image evaluation system for assessing the severity of chronic liver disease. Comput. Med. Imag. Graph. 31, 485–491(2007)CrossRefGoogle Scholar
  9. 9.
    Imen, K., Fablet, R., Boucher, J.M., Augustin, J.M.: Region- based and incidence angle dependent segmentation of seabed sonar images using a level set approach combined to local texture statistics. Asia Pacific Oceans 2006, 1–7(2007)Google Scholar
  10. 10.
    Lu, Z., Song, E., Wang, Q., Wang, X.: The liver fibrosis identification based on color 2D wavelet transform for the medical image. In: Proceedings of the International Conference on Wavelet Analysis and Pattern Recognition, pp.205–208, Hong Kong(2008)Google Scholar
  11. 11.
    Masutani, Y., Uozumi, K., Akahane, M., Ohtomo, K.: Liver CT image processing: a short introduction of the technical elements. Eur. J. Tadiol. 58, 246–251(2006)CrossRefGoogle Scholar
  12. 12.
    Pham, M., Susomboon, R., Disney, T., Raicu, D., Furst, J.: A comparison of texture models for automatic liver segmentation. In: Proceedings of SPIE Medical Imaging(2007)Google Scholar
  13. 13.
    Zhang, X., Fujita, H., Kanematsu, M., Zhou, X., Hara, T., Kato, H., Yokoyama, R., Hoshi, H.: Improving the classification of cirrhotic liver by using texture features. In: Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, pp.867–870, Shanghai, China(2005)Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Department of Computer and System SciencesSapienza University of RomeRomeItaly

Personalised recommendations