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A method of microvascular systems analysis based on statistical texture parameter's evaluation

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Języki publikacji
EN
Abstrakty
EN
It is considered a method of computer aided analysis of textures in biomedical images. The method is based on a hierarchy of simple combinatorial tests applied to squareform sub-images on several levels of image analysis. The tests are obtained as a result of combinations of two basic transformations of the original image: restructing and selection. The results of tests are then collected into numerical multi-component vectors and an analysis of vectors similarity is performed. On the basis of evaluated similarity measures a procedure of merging sub-windows covered by similar textures into compact and homogenous segments can be performed. The method is, in particular, oriented to a computer-aided analysis of textures representing micro-vascular systems.
Twórcy
  • Institute of Biocybernetics and Biomedical Engineering Polish Academy of Sciences, Warsaw, Poland
  • Institute of Biocybernetics and Biomedical Engineering Polish Academy of Sciences, Warsaw, Poland
Bibliografia
  • [1] Bruno A., Collorec R., Bezy-Wendling J., et al.: Texture Analysis in Medical Imaging. In: Contemporary Perspectives in Three-Dimensional Biomedical Imaging (pod red. Roux C. and Coatrieux J.-L.) IOS Press, Amsterdam 1997, 133-164.
  • [2] Carkacioglu A., Yarman-Vural F.T.: Similarity Measures for Binary and Gray Level Markov Random Field Textures. Image Analysis and Processing, 9,h International Conference, ICIAP’97, Florence, September 1997 Proceedings vol. I. (Del Bimbo A. ed.), 127-133.
  • [3] Chaudhuri B. B., Sarkar N.: An Efficient Approach to Compute Fractal Dimension in Texture Image. Proc. 11th IAPR International Conference on Pattern Recognition, Hague, August 1992, Vol. 1 IEEE Computer Society Press, Los Alamitos, 1992, 358-361.
  • [4] Nieniewski M.: Experiments with Morphological Markov Random Fields. Machine Graphics and Vision, 1998, 7, 1'2, 205-220.
  • [5] Ojala T., Pietikainen M.: Unsupervised Texture Segmentation Using Feature Distributions, Texture Analysis Using Pairwise Interaction Maps , Image Analysis and Processing, 9th International Conference, ICIAP'97, Florence, September 1997 Proceedings, Vol. 1 (Del Bimbo A. ed.), 311-318.
  • [6] Patel D., Stonham T.J.: Texture Image Classification and Segmentation Using RANK-Order Clustering. Proc. 11th IAPR International Conference on Pattern Recognition, Hague, August 1992, Vol. III. IEEE Computer Society Press, Los Alamitos, 1992, 92-95.
  • [7] Smith T.G., Lange G.D.: Biological Cellular Morphometry-Fractal Dimensions, Lacunarity and Multifractals. In: Fractals in Biology and Medicine, Vol. II (pod red. Losa G.A., Merlini D. et al.). Birkhauser, Basel, 1998, 30-49.
  • [8] Xiaohan Y., Yla-Jaaski J.: Unsupervised Texture Segmentation Based on the Modified Markov Random Field Model. Proc. 11th IAPR International Conference on Pattern Recognition, Hague, August 1992, Vol. III. IEEE Computer Society Press, Los Alamitos, 1992, 88-91.
  • [9] Zhu Y.M., Gao Y., Goutte R., Amiel M.: Textural boundary Detection Using Local Spatial Frequency Analysis. Proc 11th IAPR International Conference on Pattern Recognition, Hague, August 1992, Vol. III. IEEE Computer Society Press, Los Alamitos, 1992, 53-56.
  • [10] Kulikowski J.L.: Analysis of Texture Based on Non-Parametric Statistical Tests. In: Lecture Notes of the ICB Seminars - Statictics and Clinical Practice, MCB PAN, |Warsaw, June 2002, 36-41.
  • [11] Kulikowski J.L.: Pattern Recognition Based on Ambiguous Indications of Experts. In: Komputerowe Systemy Rozpoznawania KOSYR'2001. Ofic. Wyd. Politechniki Wrocławskiej, Wrocław 2001 r.
  • [12] Marek T.: Analiza skupień w badaniach empirycznych. Metody SAHN, PWN, Warszawa 1989.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPZ1-0003-0054
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