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Tytuł artykułu
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Warianty tytułu
Języki publikacji
Abstrakty
The method enhancing distinctiveness of the micro-morphological structures, developed using the properties of morphological spectra of their monochromatic 2D images, is presented and its effects on the bone section image are statistically compared with enhancements by Sobel, Roberts and Laplace high-pass filters. Comparison of different filters based on statistical parameters of the classes of selected image details is presented. The preferable method for choosing filtering weight coefficients is described and illustrated by an example of processing an electron-microscope image of a biotechnological specimen. The applicability of this approach and possible development directions are discussed.
Wydawca
Czasopismo
Rocznik
Tom
Strony
206--215
Opis fizyczny
Bibliogr. 23 poz., rys., tab.
Twórcy
autor
- Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Trojdena 4, 02-109 Warsaw, Poland
autor
- Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Trojdena 4, 02-109 Warsaw, Poland
autor
- Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Trojdena 4, 02-109 Warsaw, Poland
Bibliografia
- [1] Lei T. Statistics of medical imaging. CRC Press Inc.; 2012.
- [2] Maitre H. Image processing. Wiley; 2008. pp. 141–57.
- [3] González R, Woods R. Digital image processing. Pearson/ Prentice Hall; 2008. pp. 827.
- [4] Koschan A, Abidi M. Digital color image processing. Hoboken, NJ: John Wiley & Sons Inc.; 2008. pp. 149–74.
- [5] Marciniak A, Nieczkowski T, Obuchowicz A. Color homogram for segmentation of fine needle biopsy images. Mach Graph Vis 2008;17(1/2):153–65.
- [6] Karim MA. Optical pattern recognition systems and techniques. In: Ramakrishnan S, El Emary IMM, editors. Computational techniques and algorithms for image processing, vol. 4. Saarbrücken: LAP Lambert Acad. Publishing GmBH & Co. KG; 2010. pp. 106–47.
- [7] Sonka M, Hlavac V, Boyle R. Image processing analysis and machine vision. Toronto, Canada: Thomson; 2008.
- [8] Nixon M, Aguado A. Feature extraction & image processing. 2nd ed. London, UK: Academic Press/Elsevier; 2008. pp. 183– 237.
- [9] Demirkaya Ö, Asyali M, Sahoo P. Image processing with MATLAB: applications in medicine and biology. Taylor & Francis; 2009.
- [10] Backes A, Casanova D, Bruno O. Texture analysis and classification: a complex network-based approach. Inform Sci 2013;219(10):168–80.
- [11] Kumar S, Monio R, Rajeesh J. Linear tumor classification using co-occurrence matrices on the contourless domain. Mach Graph Vis 2011;20(2):197–214.
- [12] Smietański J, Tadeusiewicz R. Discriminatory power of co-occurrence features in perfusion CT prostate cancer. Mach Graph Vis 2010;19(2):185–99.
- [13] Kunze H, La Torre D, Mendivil F, Vrscay E. Fractal-based methods in analysis. Springer; 2011. pp. 1–79.
- [14] Canny JF. A computational approach to edge detection. Trans Pattern Anal Mach Intell 1986;8(6):679–98.
- [15] Lindeberg T. Edge detection and ridge detection with automatic scale selection. Int J Comput Vis 1998;30(21):117–54.
- [16] Kekre H, Sudeep D. Thepade & Akshay Maloo: Performance comparison of image retrieval using fractional coefficients of transformed image using DCT Walsh Haar and Kekres transform. Int J Image Process 2010;4(2):142.
- [17] Deserno T. Biomedical image processing. Berlin: Springer- Verlag; 2011. pp. 166–7.
- [18] Kulikowski J, Przytulska M, Wierzbicka D. Biomedical structures representation by morphological spectra. In: Piętka E, Kawa J, editors. Information technology in biomedicine Advances in soft computing, vol. 47. Springer- Verlag; 2008. pp. 57–65.
- [19] Kulikowski J, Przytulska M, Wierzbicka D. Description of biomedical textures by statistical properties of their morphological spectra. Biocybernet Biomed Eng 2009;30(3): 19–34.
- [20] Kulikowski J, Przytulska M, Wierzbicka D. Direct filtering and enhancement of biomedical images based on morphological spectra. In: Kurzyński M, Woźniak M, editors. Computer recognition systems 3 Advances in soft computing, vol. 57. Springer, Heidelberg; 2009. pp. 159–66.
- [21] Przytulska M, et al. Final report on realization of the Project No N N518 4211 33 ‘‘Methods of computer analysis of radiological images of pathological changes in selected inner organs’’. Warsaw: Institute of Biocybernetics and Biomedical Engineering PAS; 2010 (in Polish unpublished).
- [22] Kulikowski J, Przytulska M, Wierzbicka D. Biomedical image segmentation based on aggregated morphological spectra. Computers in medical activity Advances in soft computing, vol. 65. Heidelberg: Springer; 2009. pp. 33–40.
- [23] Russ JC. The image processing handbook, vol. 283. Boca Raton: CRC Press; 2011. pp. 433–506.
Typ dokumentu
Bibliografia
Identyfikator YADDA
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