Classification for pathological prostate images based on fractal analysis

Proceedings - 1st International Congress on Image and Signal Processing, CISP 2008

Volumn 3, Issue , 2008, Pages 113-117

  Lee, Cheng Hsiung ^a   Huang, P W ^a  

^a NATIONAL CHUNG HSING UNIVERSITY   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

CHLORINE COMPOUNDS; CLASSIFIERS; FEATURE EXTRACTION; FRACTAL DIMENSION; FRACTALS; IMAGE ENHANCEMENT; IMAGE SEGMENTATION; KETONES; LEARNING SYSTEMS; SIGNAL PROCESSING;

BAYES CLASSIFIERS; CLASSIFICATION RATES; FEATURE EXTRACTION METHODS; FRACTAL ANALYSIS; GLEASON GRADING; INTERNATIONAL CONGRESSES; K-NEAREST NEIGHBORS; K-NN CLASSIFIER; PROSTATE IMAGING; TEXTURE COMPLEXITY; VARIATIONS OF;

CLASSIFICATION (OF INFORMATION);

EID: 52149100786     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CISP.2008.609     Document Type: Conference Paper

References (26)

1. American Cancer Society, Cancer Facts & Figures 2007. Atlanta, GA: American Cancer Society, 2007.
2. J. W. Baish and P. K. Jain, "Fractals and cancer," Cancer Research, vol. 60, pp. 3683-3688, 2000.
3. C. K. Chui and J. A. Lian, "A study of orthonormal multiwavelets," Appl. Numer. Math., vol. 20, pp. 273-298, 1995.
4. R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis, New York: Wiley, 2000.
5. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2nd ed. New York: Wiley, 2001.
6. K. Fukunaga, Introduction to Statistical Pattern Recognition, San Diego, CA: Academic, 1990.
7. R. Farjam, H. Soltanian-Zadeh, R. A. Zoroofi, and K. J. Khouzani, "Tree-structured grading of pathological images of prostate," Proceedings of SPIE on Medical Imaging, vol. 5746, San Diego, CA, pp. 840-851, 2005.
8. J. S. Geronimo, D. P. Hardin, and P. R. Massopust, "Fractal functions and wavelet expansions based on several functions," J. Approx. Theory, vol. 78, no. 3, pp. 373-401, 1994.
9. E. Gose, R. Johnsonbaugh, and S. Jost, Pattern Recognition and Image Analysis. Englewood Cliffs, NJ: Prentice-Hall, 1996.
10. R. C. Gonzalez, R. E. Woods, Digital Image Processing, 2nd ed. New Jersey: Prentice-Hall, 2002.
11. K. Jafari-Khouzani and H. Soltanian-Zadeh, "Multiwavelet grading of pathological images of prostate," IEEE Transactions on Biomedical Engineering, vol. 50, no. 6, pp. 697-704, June 2003.
12. W.-Li Lee, Y.-Chang Chen, and K.-Sheng Hsieh, "Ultrasonic liver tissues classification by fractal feature vector based on M-band wavelet transform," IEEE Transactions on Medical Imaging, vol. 22, no. 3, pp. 382-392, March 2003.
13. B. B. Mandelbrot, Fractal Geometry of Nature. San Francisco, CA: Freeman, 1982.
14. M. Nadler and E. P. Smith, Pattern Recognition Engineering. New York: Wiley, 1993.
15. G. J. O'Dowd, R. W. Veltri, M. C. Miller, and S. B. Strum, "The Gleason Score: A Significant Biologic Manifestation of Prostate Cancer Aggressiveness on Biopsy," Prostate Cancer Research Institute: PCRI Insights, vol. 4, no. 1, pp. 1-5, 2001.
16. J. Rosai and L. V. Ackerman, Ackerman's Surgical Pathology. St. Louis, MO: Mosby, 1996.
17. N. Sarkar and B. B. Chaudhuri, "An efficient differential box-counting approach to compute fractal dimension of image," IEEE Transactions on Systems, Man and Cybernetics, vol. 24, no. 1, pp. 115-120, Jan 1994.
18. _, "Texture segmentation using fractal dimension," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 17, no. 1, 1995.
19. L.-X. Shen, H. H. Tan, and J. Y. Tham, "Symmetric-antisymmetric orthonormal multiwavelets and related scalar wavelets," Appl. Computational Harmonic Anal. (ACHA), vol. 8, no. 3, pp. 258-279, May 2000.
20. T. Stojic, I. Reljin, and B. Reljin, "Adaptation of multifractal analysis to segmentation of microcalcifications in digital mammograms," Physica A, vol. 367, pp. 494-508, 2006.
21. M. Teverovskiy, V. Kumar, J. Ma, A. Kotsianti, D. Verbel, A. Tabesh, H. Pang, Y. Vengrenyuk, S. Fogarasi, and O. Saidi, "Improved prediction of prostate cancer recurrence based on an automated tissue image analysis system," IEEE International Symposium on Biomedical Imaging: Macro to Nano, Arlington, VA, pp. 257-260, 2004.
22. T., Takahashi, T. Murata, k. Narita, T. Hamada, H. Kosaka, M. Omori, K. Takahashi, H. Kimura, H. Yoshida, and Y. Wada, "Multifractal analysis of deep white matter microstructural changes on MRI in relation to early-stage atherosclerosis," NeuroImage, vol. 32, no. 3, pp. 1158-1166, 2006.
23. R. F. Voss, "Random fractals: Characterization and measurement." In: R. Pynn and A. Skjeltorp, Ed., Scaling Phenomena in Disorderd Systems, Plenum, New York, 1986.
24. S. Yan, L. Jianming and Y. Takashi, "Texture classification for liver tissues from ultrasonic B-scan images using testified PNN," IEICE Transactions on Information and Systems, vol. 89, no. 8, pp. 2420-2428, 2006.
25. Y. Zhu, S. Williams, and R. Zwiggelaar, "Computer technology in detection and staging of prostate carcinoma: A review," Medical Image Analysis, vol. 10, pp. 178-199, 2006.
26. Http://info.cancerresearchuk.org/cancerstats/types/prosta te/. Cancer Research UK: UK Prostate Cancer statistics. 2007.