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Segmentation of breast cancer fine needle biopsy cytological images

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper describes three cytological image segmentation methods. The analysis includes the watershed algorithm, active contouring and a cellular automata GrowCut method. One can also find here a description of image pre-processing, Hough transform based pre-segmentation and an automatic nuclei localization mechanism used in our approach. Preliminary experimental results collected on a benchmark database present the quality of the methods in the analyzed issue. The discussion of common errors and possible future problems summarizes the work and points out regions that need further research.
Rocznik
Strony
159--170
Opis fizyczny
Bibliogr. 34 poz., rys., tab., wykr.
Twórcy
autor
  • Institute of Control and Computation Engineering, University of Zielona Góra, ul. Podgórna 50, 65–246 Zielona Góra, Poland
autor
  • Institute of Control and Computation Engineering, University of Zielona Góra, ul. Podgórna 50, 65–246 Zielona Góra, Poland
  • Institute of Control and Computation Engineering, University of Zielona Góra, ul. Podgórna 50, 65–246 Zielona Góra, Poland
  • Institute of Control and Computation Engineering, University of Zielona Góra, ul. Podgórna 50, 65–246 Zielona Góra, Poland
Bibliografia
  • [1] Arabas J. (2004). Lectures on Evolutionary Algorithms, WNT, Warsaw (in Polish).
  • [2] Ballard D. (1981). Generalizing the Hough transform to detect arbitrary shapes, Pattern Recognition 13(2): 111-122.
  • [3] Blake A., Isard M. (1998). Active Contours, Springer, London.
  • [4] Boldrini J. and Costa M. (1999). An application of optimal control theory to the design of theoretical schedules of anticancer drugs, International Journal of Applied Mathematics and Computer Science 9(2): 387-399.
  • [5] Carlotto M. (1987). Histogram analysis using a scale space approach, IEEE Transactions on Pattern Analysis and Machine Intelligence 9(1): 121-129.
  • [6] Chen C., Luo J. and Parker K. (1998). Image segmentation via adaptive K-mean clustering and knowledge-based morphological operations with biomedical applications, IEEE Transactions on Image Processing 7(12): 1673-1683.
  • [7] Duda R. and Hart P. (1972). Use of the Hough transformation to detect lines and curves in picture, Communications of Association for Computing Machinery 15(1): 11-15.
  • [8] Gonzalez R. and Woods R. (2002). Digital Image Processing, Prentice Hall, Englewood Cliffs, NJ.
  • [9] Hrebień M., Nieczkowski T., Korbicz J. and Obuchowicz A. (2006). The Hough transform and the GrowCut method in segmentation of cytological images, Proceedings of the International Conference on Signal and Electronic Systems ICSES'06, Łódź, Poland, pp. 367-370.
  • [10] Hrebień M. and Steć P. (2006). Fine needle biopsy material segmentation with Hough transform and active contouring technique, Journal of Medical Informatics and Technologies 10: 25-34, (in print).
  • [11] Hrebień M., Korbicz J. and Obuchowicz A. (2007). Hough transform, (1+1) search strategy and watershed algorithm in segmentation of cytological images, Proceedings of the 5th International Conference on Computer Recognition Systems CORES'07, Springer, Wrocław, pp. 550-557.
  • [12] Kass M., Witkin A. and Terauzopoulos D. (1987). Snakes: Active contour models, Proceedings of the 1st International Conference on Computer Vision, pp. 259-263.
  • [13] Kimmel M., Lachowicz M. and Świerniak A. (Eds.) (2003). Cancer growth and progression, mathematical problems and computer simulations, International Journal of Applied Mathematics and Computer Science 13 (3) (Special Issue).
  • [14] LeeM. and StreetW. (2000). Dynamic learning of shapes for automatic object recognition, Proceedings of the 17th Workshop Machine Learning of Spatial Knowledge, Stanford, CA, pp. 44-49.
  • [15] Madisetti V. and Williams D. (1997). The Digital Signal Processing Handbook, CRC Press, Boca Raton, FL.
  • [16] Marciniak A., Obuchowicz A., Monczak R. and Kołodziński M. (2005). Cytomorphometry of fine needle biopsy material from the breast cancer, Proceedings of the 4th International Conference on Computer Recognition Systems CORES'05, Springer, Rydzyna, Poland, pp. 603-609.
  • [17] Michalewicz Z. (1996): Genetic Algorithms + Data Structures = Evolution Programs, Springer, London.
  • [18] Otsu N. (1979). A threshold selection method from grey-level histograms, IEEE Transactions on Systems, Man and Cybernetics 9(1): 62-66.
  • [19] Pena-Reyes C. and Sipper M. (1998). Envolving fuzzy rules for breast cancer diagnosis, Proceedings of the International Symposium on Nonlinear Theory and Application, Vol. 2, Polytechniques et Universitaires Romandes Press, pp. 369-372.
  • [20 Pratt W. (2001). Digital Image Processing, Wiley, New York.
  • [21] Russ J. (1999). The Image Processing Handbook, CRC Press, Boca Raton, FL.
  • [22] Sethian J. (1999). Fast marching methods, SIAM Review 41(2): 199-235.
  • [23] Setiono R. (1996). Extracting rules from pruned neural networks for breast cancer diagnosis, Artificial Intelligence in Medicine 8 (1): 37-51.
  • [24] Steć P. and Domański M. (2005). Video frame segmentation using competitive contours, Proceedings of the 13th European Signal Processing Conference EUSIPCO'05, Antalya, Turkey, pp. 4 (CD-ROM).
  • [25] Street W. (2000). Xcyt: A system for remote cytological diagnosis and prognosis of breast cancer, in: (Jain L. (Ed.)), Soft Computing Techniques in Breast Cancer Prognosis and Diagnosis, World Scientific Publishing, Singapore, pp. 297-322.
  • [26] Su M. and Chou C. (2001). A modified version of the K-means algorithm with a distance based on cluster symmetry, IEEE Transactions Pattern Analysis and Machine Intelligence 23(6): 674-680.
  • [27] Świerniak A., Ledzewicz U. and Schättler H. (2003). Optimal control for a class of compartmental models in cancer chemotherapy, International Journal of Applied Mathematics and Computer Science 13(3): 357-368.
  • [28] Tadeusiewicz R. (1992). Vision Systems of Industrial Robots, WNT, Warsaw, (in Polish).
  • [29] Vezhnevets V. and Konouchine V. (2005). "GrowCut" - interactive multi-label N-D image segmentation by cellular automata, Proceedings of the 15th International Conference on Computer Graphics and Applications GraphiCon'05, Novosibirsk, Russia, pp. 150-156.
  • [30] Vincent L. and Soille P. (1991).Watersheds in digital spaces: An efficient algorithm based on immersion simulations, IEEE Transactions on Pattern Analysis and Machine Intelligence 13(6): 583-598.
  • [31] Wolberg W., Street W. and Mangasarian O. (1993). Breast cytology diagnosis via digital image analysis, Analytical and Quantitative Cytology and Histology 15(6): 396-404.
  • [32] Zhang J. (1996). A survey on evaluation methods for image segmentation, Pattern Recognition 29(8): 1335-1346.
  • [33] Zhou P. and Pycock D. (1997). Robust statistical models for cell image interpretation, Image and Vision Computing 15(4): 307-316.
  • [34] Żorski W. (2000). Image Segmentation Methods Based on the Hough Transform, Studio GiZ, Warsaw, (in Polish).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPZ1-0044-0015
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