Classifying Pathological Prostate Images by Fractal Analysis

  • Po-Whei Huang
  • Cheng-Hsiung Lee
  • Phen-Lan Lin


This study presents an automated system for grading pathological prostate images based on texture features of multicategories including multiwavelets, Gaborfilters, gray-level co-occurrence matrix (GLCM) and fractal dimensions. Images are classified into appropriate grades by using k-nearest neighbor (k-NN) and support vector machine (SVM) classifiers. Experimental results show that a correct classification rate (CCR) of 93.7 % (or 92.7 %) can be achieved by fractal dimension (FD) feature set by using k-NN (or SVM) classifier without feature selection. If the FD feature set is optimized, the CCR of 94.2 % (or 94.1 %) can be achieved by using k-NN (or SVM) classifier. The CCR is promoted to 94.6 % (or 95.6 %) by k-NN (or SVM) classifier if features of multicategories are applied. On the other hand, the CCR drops if the FD-based features are removed from the combined feature set of multicategories. Such a result suggests that features of FD category are not negligible and should be included for consideration for classifying pathological prostate images.


Support Vector Machine Fractal Dimension Feature Selection Texture Feature Support Vector Machine Classifier 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    American Cancer Society: Cancer Facts & Figures 2007. American Cancer Society, Atlanta, GA (2007)Google Scholar
  2. 2.
    Zhu, Y., Williams, S., Zwiggelaar, R.: Computer technology in detection and staging of prostate carcinoma: a review. Med. Image Anal. 10, 178–199 (2006)CrossRefGoogle Scholar
  3. 3.
    Gleason, D.F.: The veteran’s administration cooperative urologic research group: histologic grading and clinical staging of prostatic carcinoma. In: Tannenbaum, M. (ed.) Urologic Pathology: The Prostate, pp. 171–198. Lea and Febiger, Philadephia, PA (1977)Google Scholar
  4. 4.
    Jafari-Khouzani, K., Soltanian-Zadeh, H.: Multiwavelet grading of pathological images of prostate. IEEE Trans. Biomed. Eng. 50, 607–704 (2003)Google Scholar
  5. 5.
    Baish, J.W., Jain, R.K.: Fractals and cancer. Cancer Res. 60, 3683–3688 (2000)Google Scholar
  6. 6.
    Sarkar, N., Chaudhuri, B.B.: An efficient differential box-counting approach to compute fractal dimension of image. IEEE Transa. Syst. Man Cybern. 24, 115–120 (1994)CrossRefGoogle Scholar
  7. 7.
    Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, New York (1990)zbMATHGoogle Scholar
  8. 8.
    Pudil, P., Novovicova, J., Kittler, J.: Floating search methods in feature selection. Pattern Recognit. Lett. 15, 1119–1125 (1994)CrossRefGoogle Scholar
  9. 9.
    Shen, L.X., Tan, H.H., Tham, J.Y.: Symmetric-antisymmetric orthonormal multiwavelets and related scalar wavelets. Appl. Comput. Harmon. Anal. (ACHA) 8, 258–279 (2000)MathSciNetzbMATHCrossRefGoogle Scholar
  10. 10.
    Jain, A.K., Farrokhnia, F.: Unsupervised texture segmentation using Gabor filters. Pattern Recognit. 24, 1167–1186 (1991)CrossRefGoogle Scholar
  11. 11.
    Pichler, O., Teuner, A., Hosticha, B.J.: A comparison of texture feature extraction using adaptive Gabor filtering, pyramidal and tree structured wavelet transforms. Pattern Recognit. 29, 733–742 (1996)CrossRefGoogle Scholar
  12. 12.
    Chaudhuri, B.B., Sarkar, N.: Texture segmentation using fractal dimension. IEEE Trans. Pattern Anal. Mach. Intell. 17, 72–77 (1995)CrossRefGoogle Scholar
  13. 13.
    Huang, P.W., Lee, C.H.: Automatic classification for pathological prostate images based on fractal analysis. IEEE Trans. Med. Imaging 28, 1037–1050 (2009)CrossRefGoogle Scholar
  14. 14.
    Kantardzic, M.: Data Mining: Concepts, Models, Methods, and Algorithms. Wiley, New Jersey (2002)CrossRefGoogle Scholar
  15. 15.
    Wu, T.F., Lin, C.J., Weng, R.C.: Probability estimates for multi-class classification by pairwise coupling. J. Mach. Learn. Res. 5, 975–1005 (2004)MathSciNetzbMATHGoogle Scholar
  16. 16.
    Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. Software (2001)
  17. 17.
    Lee, W.L., Chen, Y.C., Hsieh, K.S.: Ultrasonic liver tissues classification by fractal feature vector based on M-band wavelet transform. IEEE Trans. Med. Imaging 22, 382–392 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringNational Chung Hsing UniversityTaichungRepublic of China
  2. 2.Department of Computer Science and Information ManagementProvidence University, ShaluTaichungRepublic of China

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