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Tytuł artykułu

Role of image processing in the cancer diagnosis

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Cancer is still one of the most deadly diseases. It is a well known fact that the early diagnosis is crucial and allows for the successful treatment while cancers diagnosed in their late stage are almost impossible to treat. For precise and objective diagnosis there is a need for a computerized method for cytological image processing, which is an integral part of a diagnosis process. In this work we present a classification system for grading cancer malignancy. In particular, issues of image processing in the aspect of medical diagnosis presented by prof. R. Tadeusiewicz and Dr. J. Śmietański in [1].
Rocznik
Strony
5--9
Opis fizyczny
Bibliogr. 21 poz., zdj.
Twórcy
autor
  • Institute of Materials Science and Applied Mechanics Wrocław University of Technology Smoluchowskiego 25, 50-370 Wrocław, Poland
autor
  • Wrocław School of Applied Informatics Wejherowska 28, 54-239 Wrocław, Poland
  • Institute of Agricultural Engineering Wrocław University of Environmental and Life Sciences Chełmońskiego 37-41, 51–630 Wrocław, Poland
autor
  • Department of Pathology and Clinical Cytology Medical University of Wrocław, Borowska 213, 50-556 Wrocław, Poland
Bibliografia
  • 1. Tadeusiewicz R., Śmietański J.. Pozyskiwanie obrazów medycznych oraz ich przetwarzanie, analiza, automatyczne rozpoznawanie i diagnostyczna interpretacja. Kraków: Wydawnictwo STN, 2011.
  • 2. Tadeusiewicz R.: Informatyka medyczna. Lublin: Wydawnictwo UMCS, 2011.
  • 3. Jeleń Ł., Lipiński A., Detyna J., Jeleń M.: Grading breast cancer malignancy with neural networks. Bio-Algorithms and Med-Systems 2011, 7, 2: 47-53.
  • 4. Bloom H.J.G., Richardson W.W.: Histological grading and prognosis in breast cancer. British Journal of Cancer 1957, 11:359–377.
  • 5. Le Doussal, V., Tubiana-Hulin, M., Friedman S., et al.: Prognostic value of histologic grade nuclear components of Scarff–Bloom–Richardson (sbr). An improved score modification based on a multivariate analysis of 1262 invasive ductal breast carcinomas. Cancer 1989, 64(9): 1914-1921
  • 6. Street N.W.: Xcyt: a system for remote cytological diagnosis and prognosis of breast cancer. In: Jain L.C. (ed.), Soft Computing Techniques in Breast Cancer Prognosis and Diagnosis, Singapore: World Scientific Publishing, 2000, pages 297–322.
  • 7. Street W.N., Wolberg W.H., Mangasarian O.L.: Nuclear feature extraction for breast tumor diagnosis. In: Imaging Science and Technology/Society of Photographic Instrumentation Engineers 1993 International Symposium on Electronic Imaging: Science and Technology, IS&T/SPIE, San Jose, California, 1993, Vol. 1905: 861–870.
  • 8. Nezafat R., Tabesh A., Akhavan S., Lucas C., Zia M.A.: Feature selection and classification for diagnosing breast cancer. Proceedings of International Association of Science and Technology for Development International Conference, IASTED, Cancun, Mexico, 1998, pp. 310–313.
  • 9. Walker H.L. Jr., Albertelli L.E.: Breast cancer screening using evolved neural networks. IEEE International Conference on Systems, Man, and Cybernetics, San Diego, USA, 1998, 2:1619–1624.
  • 10. Cheng H.D., Li X.Q., Riodan D., Scrimger J.N.: A Parallel Approach to Tubule Grading in Breast Cancer Lesions and its VLSI Implementation. Computer-Based Medical Systems: Fourth Annual IEEE Symposium, 1991, pp. 322–329.
  • 11. Schnorrenberg F., Tsapatsoulis N., Pattichis C.S., Schizas C.N., Kollias S., Vassiliou M., Adamou A., Kyriacou K.: A modular neural network system for the analysis of nuclei in histopathological sections. IEEE Engineering in Medicine and Biology Magazine 2000, 19: 48–63.
  • 12. Jeleń Ł., Fevens T., Krzyżak A.: Classification of Breast Cancer Malignancy using Cytological Images of Fine Needle Aspiration Biopsies. Int. J. Appl. Math. Comput. Sci. 2008, 18, 1: 75–83.
  • 13. Ridler, T., Calvard, S.: Picture thresholding using an iterative selection. IEEE Trans. System, Man and Cybernetics 1978, 8: 630–632.
  • 14. Klir G.J., Yuan B.: Fuzzy sets and fuzzy logic: Theory and applications. New Jersey: Prentice Hall, 1995.
  • 15. Theera-Umpon N:. Patch–Based white blood cell nucleus segmentation using fuzzy clustering. ECTI Transactions on electrical eng., electronics and communications 2005, 3(1):15–19.
  • 16. Bezdek J.C.: Pattern recognition with fuzzy objective function algorithms. New York: Plenum Press, 1981.
  • 17. Jeleń Ł.: Computerized cancer malignancy grading of fine needle aspirates. PhD thesis, Concordia University, 2009.
  • 18. Rangayyan R.M.: Biomedical Image Analysis (Biomedical Engineering). Boca Raton, FL: CRC Press, 2004.
  • 19. Lapedes A. Farber R.: Nonlinear signal processing using neural networks: prediction, and system modelling. Technical Report, LA–UR–87–2662, 1987.
  • 20. Wodzisławski W., Detyna J., Jeleń Ł., Kaczyński R.: Ocena wybranych metod klasyfikacji pacjentów (naiwna metoda Bayesa, metoda wektorów nośnych) w aspekcie reakcji kości miednicznej na wszczepienie panewki stawu biodrowego. Sci. Bull. of Chełm. Sect. of Math. and Comp. Science 2010, 1: 179-212.
  • 21. Scarff R.W., Torloni H.: Histological typing of breast tumors. International histological classification of tumours. World Health Organization. Geneva 1968, 2, 2: 13-20.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-309792d2-49fc-42a3-87e7-983047ec074d
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