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Abstrakty
In this paper, two image processing methods for use in medical image processing based on the level set method are described. The theoretical bases are described and the methods are applied to a set of sample computed tomography images. The results are then compared. The results indicate that the Chan-Vese method is more useful for image segmentation in medicine than the distanceregulated method owing to both the significant differences in calculation time and the quality of results obtained for noisy images.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
47--51
Opis fizyczny
Bibliogr. 12 poz., rys., zdj.
Twórcy
autor
- MSc, Institute of Electronics, Lublin University of Technology, Nadbystrzycka 38a 20-618 Lublin, Poland
autor
- Institute of Electronics, Lublin University of Technology, Lublin, Poland
autor
- Pneumonology, Oncology and Allergology Department, Medical University of Lublin, Lublin, Poland
autor
- Department of Human Physiology, Medical University of Lublin, Lublin, Poland
Bibliografia
- 1. Roukos DH, Agnantis NJ. Gastric cancer: diagnosis, staging, prognosis. Gastric Breast Cancer 2002;11:7–10.
- 2. Reddy LS, Redd R, Madhu CH, Nagaraju C. A novel image segmentation technique for detection of breast cancer. Int J Inf Technol Knowl Manage 2010;2:201–4.
- 3. American Cancer Society. Cancer facts & figures 2010. Atlanta, GA: American Cancer Society, 2010.
- 4. Pham DL, Xu C, Prince JL.Current methods in medical image segmentation. Ann Rev Biomed Eng 2000;2:315–37.
- 5. Kotyra A, Sawicki D, Gromaszek K, Smolarz A. Wykorzystanie konturu aktywnego do określania obszaru płomienia w wizyjnym systemie diagnostycznym. Elek Konst Technol Zastosowania 2012;6:27–8.
- 6. Rymarczyk T. Zastosowanie metody zbiorow poziomicowych w tomografii impedancyjnej. PhD thesis, Warsaw, 2010.
- 7. Osher S, Fedkiw R. Level set methods and dynamic implicit surfaces. New York: Springer, 2003.
- 8. Sethian JA. Level set methods and fast marching methods. Cambridge: Cambridge University Press, 1999.
- 9. Li C, Xu C, Gui C, Fox MD. Distance regularized level set evolution and its application to image segmentation. IEEE Trans Image Proc 2010;19:3243–54.
- 10. Chen TF. Medical image segmentation using level sets. Technical report #CS-2008-12, University of Waterloo, May 2008.
- 11. Droskey M, Meyerz B, Rumpfy M, Schallerz K. An adaptive level set method for medical image segmentation. Bonn, Germany: Institut fur Angewandte Mathematik, Klinik fur Neurochirurgie, Universitat Bonn.
- 12. Wu Y. PhD. Interdisciplinary Laboratory for Computation, Tufts University, Medford, MA USA. Available at:https://sites.google.com/site/rexstribeofimageprocessing/chan-vese-active-contours. Accessed on 23 October 2014.
Typ dokumentu
Bibliografia
Identyfikator YADDA
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