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Discrimination of biomedical textures based on logical similarity measure

Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper presents an approach to discrimination of textures in radiological images based on multi-aspect similarity measures composed of logical tests. There are formulated basis assumptions for similarity measures which can be composed by products of partial (single-aspect) similarity measures. On the basis of similarity measures -similarity classes are defined. Next, two types: strong and weak similarity measures are defined. It is shown that they make possible to define similarity measures based on quality objects properties as well as on their numerical parameters. As an example of application of the general concept discrimination of normal and ill (lesions affected) tissues is considered. It is illustrated by analysis of USG images of liver tissues for which morphological spectra and their statistical parameters have been calculated. It is shown that the differences between values of some pairs of corresponding parameters can be used to a construction of an effective algorithm of textures discrimination. This algorithm takes into consideration both, numerical features of the texture samples and some qualitative data concerning the patients. Conclusions are formulated at the end of the paper.
Rocznik
Tom
Strony
89--95
Opis fizyczny
Bibliogr. 21 poz., rys., tab.
Twórcy
  • Nalecz Institute of Biocybernetics and Biomedical Engineering PAS, 4, Ks. Trojdena Str., 02-109 Warsaw, Poland
Bibliografia
  • [1] CAMPISI G., JACOVITTI G., NERI A., Optimized wold-like decomposition of 2D random fields, Proc. Eur. Sig. Proc. Conf. EUSPICO’98, Island of Rhodes, 1998, pp. 1681-1684.
  • [2] CHANG T., KUO C.C.J., Texture analysis and classification with tree-structured wavelet domain, IEEE Trans. Image Processing, Vol. 2, 1993, pp. 429-441.
  • [3] CHELLAPPA R., CHATTERJEE S., Classification of texture using Gaussian Markov random fields, IEEE Trans. Acoust. Speech, Signal Processing, Vol. 29, 1985, pp. 110-1129.
  • [4] COHEN F.S., FAN Z., PATEL M.A., Classification of rotated and scaled textured images using Gaussian Markov random field models, IEEE Trans. Pattern Anal. Machine Intell., Vol. 13, No. 2, 1991, pp. 192-202.
  • [5] CROSS G.R., JAIN A.K., Markov random field texture models, IEEE Trans. Pattern Anal. Machine Intell., Vol. 5, 1983, pp. 25-39.
  • [6] FAN Y., JIANG T., EVANS D.J., Volumetric segmentation of brain images using parallel genetic algorithms, IEEE Trans. on Medical Imaging, Vol. 21, No. 8, 2002, pp. 904-909.
  • [7] FARAG A.A., AHMED M.N., EL-BAZ A, HASSAN H., Advances Segmentation Techniques, in SURI J.S., WILSON D.L., LAXMINARAYAN S., (eds.), Handbook of Biomedical Image Analysis, Vol. I: Segmentation Models, Part A. Kluwer Academic/Plenum Publishers, New York, 2005.
  • [8] GHOSH P., MITCHELL M., Segmentation of medical images using a genetic algorithm, Proc. of the 8th Annual Conf. on Genetic and Evolutionary Computation, Seattle, USA, 2006, pp. 1171-1178.
  • [9] KAPLAN L.M., Extended fractal analysis for texture classification and segmentation, IEEE Trans. Image Processing, Vol. 8, 1999, pp. 1572-1585.
  • [10] KOBASHI S., KAMIURA N., HATA Y., MIYAWAKI F., Volume-quantization-based neural network approach to 3D MR angiography image segmentation, Image and Vision Computing , Vol. 19, No. 4, 2001, pp. 184-195.
  • [11] KULIKOWSKI J.L., From pattern recognition to image interpretation, Biocybernetics and Biomedical Engineering, Vol. 22, No. 2-3, 2002, pp. 177-197.
  • [12] KULIKOWSKI J.L., PRZYTULSKA M., WIERZBICKA D., Description of biomedical textures by statistical properties of morphological spectra, Biocybernetics and Biomed. Eng., Vol. 30, No. 3, 2010, pp. 19-34.
  • [13] KULIKOWSKI J.L., PRZYTULSKA M., WIERZBICKA D., Morphological Spectra as Tools for Texture Analysis, in: M. KURZYNSKI & al. (Eds.), Computer Recognition Systems 2, LNSC 45, Springer-Verlag, Berlin, 2007, pp. 510-517.
  • [14] KURNAZ M.N., DOKUR Z., OLMEZ T., An incremental neural network for tissue segmentation in ultrasound images, Computer Methods and Programs in Biomedicine, Vol. 85, No. 3, 2007, pp. 187-195.
  • [15] LIU F., PICARD R.W., Periodicity, directionality, and randomness, wold features for image modeling and retrieval, IEEE Trans. Pattern Anal. Machine Intell., Vol. 18, 1996.
  • [16] LUCHT R., DELORME S., BRIX G., Neural network-based segmentation of dynamic MR mammographic images, Magnetic Resonance Imaging, Vol. 20, No. 2, 2002, pp. 147-154.
  • [17] PORTER R., CANAGARAJAH N., Log-polar wavelet energy signatures for rotation and scale invariant texture classification, IEEE Trans. Pattern Anal. Machine Intell., Vol. 25, No. 5, 2003, pp. 590-603.
  • [18] PRZYTULSKA M. (Head), Report N N518 4211 33 of the Project on Methods of computer analysis of radiological images for patho-morphological lesions assessment in selected inner body organs, IBBE PAS, Warsaw, 2010, (in Polish).
  • [19] REINHARDT F., SOEDER H., Atlas Mathematik, Deutscher Taschenbuch Verlag, Munich, 2001.
  • [20] TAO W.B., TIAN J.W., LIU J., Image segmentation by three-level thresholding based on maximum fuzzy entropy and genetic algorithm, Pattern Recogn. Letters, Vol. 24, No. 16, 2003, pp. 3069-3078.
  • [21] UNSER M., Texture classification and segmentation using wavelet frames, IEEE Image Processing, Vol. 4, No. 11, 1995, pp. 1549-1560.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-PWA4-0016-0009
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