Independent Component Analysis of Textures in Angiography Images

The technique of independent component analysis (ICA) is applied for texture feature detection. In ICA an optimal transformation (with respect to the statisti- cal structure of the image samples set) is discovered via blind signal processing. Any texture is considered as a mixture of independent sources (basic functions of detected transformation). Experimental comparison is documented on the compactness and separability of base functions, the data-specific ICA-based and universal Gabor functions (the latter are set by default for all kinds of images).

[1]  Erkki Oja,et al.  Independent Component Analysis , 2001 .

[2]  Heinrich Niemann.  Pattern Analysis and Understanding , 1990 .

[3]  Mika Inki,et al.  ICA FEATURES OF IMAGE DATA IN ONE, TWO AND THREE DIMENSIONS , 2003 .

[4]  Juyang Weng,et al.  Using Discriminant Eigenfeatures for Image Retrieval , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Włodzimierz Kasprzak,et al.  Adaptive computation methods in digital image sequence analysis , 2000 .

[6]  Anil K. Jain,et al.  Unsupervised texture segmentation using Gabor filters , 1990, 1990 IEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings.

[7]  Phil Brodatz,et al.  Textures: A Photographic Album for Artists and Designers , 1966 .

[8]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[9]  Andrzej Cichocki,et al.  Adaptive blind signal and image processing , 2002 .

[10]  M. R. Turner,et al.  Texture discrimination by Gabor functions , 1986, Biological Cybernetics.

[11]  Phil Brodatz.  Land, Sea, and Sky: A Photographic Album for Artists and Designers , 1976 .

[12]  Trygve Randen,et al.  Filtering for Texture Classification: A Comparative Study , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Andrzej Cichocki,et al.  Hidden image separation from incomplete image mixtures by independent component analysis , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[14]  Andrzej Cichocki,et al.  Neural network approach to blind separation and enhancement of images , 1996, 1996 8th European Signal Processing Conference (EUSIPCO 1996).

[15]  Robert Jenssen,et al.  ICA FILTER BANK FOR SEGMENTATION OF TEXTURED IMAGES , 2003 .

[16]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.