Segmentation of images using gradient methods and polynomial approximation

• Autorzy: Piekar E. , Momot M. , Momot A.
• Języki publikacji: EN

Abstrakty

EN

The paper presents a method for segmentation of images using region growing, with modification through the use of a correction coefficient based on the variation of intensity (brightness) in the neighborhood of the pixel of the interest. A method for the quantification of variability is based on differences in intensity, as well as the differences in intensity gradients in the surrounding pixels [10]. Evaluation of the gradients were determined by means of numerical differentiation, using the polynomial approximation [11]. The article presents the effects of application of developed methods for segmentation of images of the brain, lungs and heart.

Słowa kluczowe

EN

segmentation; gradient methods; polynomial approximation

PL

segmentacja; metody gradientowe; przybliżenie wielomianowe

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2014
• Tom: Vol. 23
• Strony: 95--102
• Opis fizyczny: Bibliogr. 11 poz., rys.

Twórcy

autor

Piekar E.

• Institute of Medical Technology and Equipment, 118 Roosevelt St., 41-800 Zabrze, Poland

autor

Momot M.

• Institute of Medical Technology and Equipment, 118 Roosevelt St., 41-800 Zabrze, Poland

autor

Momot A.

• Silesian University of Technology, Institute of Computer Science, 16 Akademicka St., 44-100 Gliwice, Poland

Bibliografia

• [1] CANNY J., A Computational Approach to Edge Detection. IEEE, 1986, Vol.8, No.6, pp. 679-698.
• [2] EURORAD, Radiological Case Database, http://www.eurorad.org/, 2014.
• [3] FABIJAŃSKA A., Results of Applying Two-Pass Region Growing Algorithm for Airway Tree Segmentation to MDCT Chest Scans from EXACT Database, The Second International Workshop on Pulmonary Image Analysis, CreateSpace, USA, 2009, pp. 251-260.
• [4] GONZALEZ R. C., WOODS R. E., Digital image processing. Pearson Education, 2008.
• [5] HAFIZ D. A., SHETA W. M., BAYOUMI S., BAYUMY A. B., A New Approach for 3D Range Image Segmentation using Gradient Method. Journal of Computer Science, 2011, Vol.7, pp. 475-487.
• [6] MANCAS M., GOSSELIN B., MACQ B., Segmentation using a region growing thresholding.Proc. of the Electronic Imaging Conference of the International Society for Optical Imaging (SPIE/EI 2005), San Jose (California, USA), 2005.
• [7] PAŁCZYŃSKI K., Segmentation on the basis of the field of digital motion image sequences, (Dissertation). Kraków, 2002. (in Polish).
• [8] PREETHA M.M.S.J, SURESH L.P., BOSCO M.J. Image segmentation using seeded region growing. Computing, Electronics and Electrical Technologies (ICCEET), International Conference, 2012, ISBN:978-1-4673-0211-1, pp. 576-583.
• [9] YOO S. T., Insight into images. Principles and practice for segmentation, registration,and image analysis. A. K. Peters, 2004, ISBN:1568812175.
• [10] ZHANG Y., Advanced Differential Quadrature Methods. CRC Press, Boca Raton, 2009, ISBN: 978-1-4200-8248-7.
• [11] ZEYUN Yu., BAJAJ C., Image segmentation using gradient vector diffusion and region merging. IEEE, 2002, Vol.2, ISSN: 1051-4651, pp. 941-944.