Orthogonal Moments Based Texture Analysis of CT Liver Images

Orthogonal moments such as Zernike moments and Legendre moments have been proven to have superior feature representation capability and low information redundancy. The number of orthogonal moments to be used as features or numerical attributes to perform any application is minimal due to the orthogonal nature. However, the information possessed by each moment order needs to be analysed to identify the appropriate moment orders for the undertaken task. In this work, a statistical significance test has been performed to select the best moment orders to discriminate normal and abnormal tissues in liver images. The experimental results reveal the efficacy of the proposed features.

[1]  Chee-Way Chong,et al.  A comparative analysis of algorithms for fast computation of Zernike moments , 2003, Pattern Recognit..

[2]  Hassan Foroosh,et al.  Extension of phase correlation to subpixel registration , 2002, IEEE Trans. Image Process..

[3]  Shree K. Nayar,et al.  Telecentric Optics for Focus Analysis , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Trevor Darrell,et al.  Pyramid based depth from focus , 1988, Proceedings CVPR '88: The Computer Society Conference on Computer Vision and Pattern Recognition.

[5]  Marek Kretowski,et al.  Modeling hepatic enhancement in computer tomography for texture interpretation , 2002, Proceedings IEEE International Symposium on Biomedical Imaging.

[6]  P. Asvestas,et al.  A fractal analysis of CT liver images for the discrimination of hepatic lesions: a comparative study , 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[7]  Alan G. Hawkes,et al.  A handbook of numerical and statistical techniques , 1977 .

[8]  B. N. Chatterji,et al.  An FFT-based technique for translation, rotation, and scale-invariant image registration , 1996, IEEE Trans. Image Process..

[9]  Josef Kittler,et al.  Floating search methods in feature selection , 1994, Pattern Recognit. Lett..

[10]  K. R. Ramakrishnan,et al.  Fast computation of Legendre and Zernike moments , 1995, Pattern Recognit..

[11]  Josef Kittler,et al.  Floating search methods for feature selection with nonmonotonic criterion functions , 1994, Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 3 - Conference C: Signal Processing (Cat. No.94CH3440-5).

[12]  von F. Zernike.  Beugungstheorie des schneidenver-fahrens und seiner verbesserten form, der phasenkontrastmethode , 1934 .

[13]  M. Gletsos,et al.  Classification of hepatic lesions from CT images using texture features and neural networks , 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[14]  A. H. Mir,et al.  Texture analysis of CT images , 1995 .

[15]  Anil K. Jain,et al.  39 Dimensionality and sample size considerations in pattern recognition practice , 1982, Classification, Pattern Recognition and Reduction of Dimensionality.

[16]  C. D. Kuglin,et al.  The phase correlation image alignment method , 1975 .

[17]  Lisa M. Brown,et al.  A survey of image registration techniques , 1992, CSUR.

[18]  G. S. Panayiotakis,et al.  Application of a neural network and four statistical classifiers in characterizing small focal liver lesions on CT , 1996, Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[19]  T. Higuchi,et al.  A Sub-Pixel Correspondence Search Technique for Computer Vision Applications , 2004 .

[20]  M. Teague.  Image analysis via the general theory of moments , 1980 .