Independent component analysis in angiography images

• Autorzy: Snitkowska E. , Kasprzak W.
• Języki publikacji: EN

Abstrakty

EN

An important source for information about digital content is the texture of image regions. This paper presents a feature extraction approach that is based on independent component analysis (ICA). In ICA a transformation of measured vectored time series is discovered via blind signal processing that gives statistically independent source signals. In our approach every textured region is considered as a mixture of statistically independent source regions, scanned to 1-D time series. After these sources, called independent components, are extracted by ICA, optimally for given image type, the mixing coefficients of particular region constitute its feature vector. The quality of such features is experimentally verified and compared to other common feature schemas. The comparison procedure explores the Fisher information criterion and classification results for feature evaluation. Our application field is the analysis of angiography images. It is difficult for medical doctors properly to classify such images, hence an automatic tool could provide support in this matter. We demonstrate the usefulness of ICA-based features for automatic evaluation of angiography images.

Słowa kluczowe

EN

langiography images; feature vectors; image analysis; independent component analysis; texture description

• Wydawca: Polish Academy of Sciences, Committee of Automatic Control and Robotics
• Czasopismo: Archives of Control Sciences
• Rocznik: 2006
• Tom: Vol. 16, no. 3
• Strony: 283--296
• Opis fizyczny: Bibliogr. 19 poz., rys., tab.

Twórcy

autor

Snitkowska E.

autor

Kasprzak W.

• Warsaw University of Technology, Institute of Control and Computation Engineering, Nowowiejska 15/19, 00-665 Warsaw,

Bibliografia

• [1] M. J. BASTIAANS: Gabor’s expansion and the Zak transform for continuous-time and discrete-time signals. In: J.Zeevi, R.Coifman (Ed.) Signal and image representation in combined spaces, Academic Press Inc., 1995, 1-43.
• [2] A. CICHOCKI and A. S. AMARI: Adaptive blind signal and image processing. John Wiley, Chichester, UK, 2003 (2nd ed.).
• [3] A. CICHOCKI and W. KASPRZAK: Nonlinear learning algorithms for blind separation of natural images. Neural Network World, IDG Co. Prague, 4 1996, 515-523.
• [4] R. A. FISHER: The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7 1936, 179-188.
• [5] R. M. HARALICK et al.: Textural features for image classification. IEEE Trans. Systems, Man and Cybernetics, 3(6), (1973), 610-621.
• [6] A. HYVÄRINEN: A family of fixed-point algorithms for independent component analysis. Proc. IEEE Mt. Conf on Acoustic, Speech and Signal Processing, Munich, Germany, (1997), 3917-3920.
• [7] A. HYVÄRINEN: Survey on independent component analysis. Neural Computing Surveys, 2 1999, 94-128.
• [8] A. HYVÄRINEN, J. KARHUNEN and E. OJA: Independent component analysis. John Wiley & Sons, New York, 2001.
• [9] A. HYVÄRINEN and E. OJA: A fast fixed-point algorithm for independent component analysis. Neural Computation, 9(7), (1997), 1483-1492.
• [10] R. JENSSEN and T. ELTOFT: ICA filter bank for segmentation of textured images. Proc. Mt. Workshop on Independent Component Analysis and Blind Source Separation, Nara, Japan, (2003), 827-832.
• [11] B. S. MANJUNATH and W. Y. MA: Texture features for browsing and retrieval of image data. IEEE Trans. Pattern Analysis and Machine Intelligence, 18(8), 1996, 837-842.
• [12] T. RADEN and J. H. HUSOY: Filtering for texture classification: A comparative study. IEEE Trans. on Pattern Analysis and Machine Intelligence, 21(4), (1999), 291-310.
• [13] K. R. RAO and R. Y IP: Discrete cosine transform - algorithms, advantages and applications. Academic Press Inc, San Diego, 1990.
• [14] J. SCHURMANN: Pattern classification. A unified view of statistical and neural approaches. John Wiley & Sons, New York, 1996.
• [15] W. SKARBEK, K. KUCHARSKI and M. BOBER: Dual linear discriminant analysis for face recognition. Fundamenta Informaticae, 61(1), (2004), 303-334.
• [16] E. SNITKOWSKA and W. KASPRZAK: Independent component analysis of textures in angiography images. In: K. Wojciechowski et al. (Ed) Computer Vision and Graphics, ICCVG 2004, Springer series: Computational Imaging and Vision, 32 (2005), 367-372.
• [17] M. TURK and A. PENTLAND: Eigenfaces for recognition. J. Cognitive Neuroscience, 3 (1991), 71-86.
• [18] R. P. WILDES: Iris recognition: an emerging biometric technology. Proc. of the IEEE, 85, (1997), 1348-1363.
• [19] P. Wu, Y. M. RO, C. S. WON and Y. CHOI: Texture descriptors in MPEG-7. In: Skarbek W. (Ed.), Computer Analysis of Images and Patterns, 2124, 2001, 21-28.