Comparison between 2D and 3D Local Binary Pattern Methods for Characterisation of Three-Dimensional Textures

Our purpose is to extend the Local Binary Pattern method to three dimensions and compare it with the two-dimensional model for three-dimensional texture analysis. To compare these two methods, we made classification experiments using three databases of three-dimensional texture images having different properties. The first database is a set of three-dimensional images without any distorsion or transformation, the second contains additional gaussian noise. The last one contains similar textures as the first one but with random rotations according x, y and z axis. For each of these databases, the three-dimensional Local Binary Pattern method outperforms the two-dimensional approach which has more difficulties to provide correct classifications.

[1]  Dinggang Shen,et al.  Deformable segmentation of 3-D ultrasound prostate images using statistical texture matching method , 2006, IEEE Transactions on Medical Imaging.

[2]  Jian Fan,et al.  Texture Classification by Wavelet Packet Signatures , 1993, MVA.

[3]  Hamid Soltanian-Zadeh,et al.  Comparison of 2D and 3D wavelet features for TLE lateralization , 2004, SPIE Medical Imaging.

[4]  Anil K. Jain,et al.  Markov random fields : theory and application , 1993 .

[5]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

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

[7]  R.M. Haralick,et al.  Statistical and structural approaches to texture , 1979, Proceedings of the IEEE.

[8]  Anil K. Jain,et al.  Texture Segmentation Using Voronoi Polygons , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Michael Unser,et al.  Texture classification and segmentation using wavelet frames , 1995, IEEE Trans. Image Process..

[10]  R. Murphy,et al.  Robust classification of subcellular location patterns in high resolution 3D fluorescence microscope images , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[11]  Yoshitomo Yaginuma,et al.  Classification of solid textures using 3D mask patterns , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[12]  Grégoire Toussaint,et al.  Three dimensional texture analysis in MRI: a preliminary evaluation in gliomas. , 2003, Magnetic resonance imaging.

[13]  G. Stachowiak,et al.  A Comparison of Texture Feature Extraction Methods for Machine Condition Monitoring and Failure Analysis , 2005 .

[14]  Matti Pietikäinen,et al.  Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[17]  Luc Van Gool,et al.  Texture analysis Anno 1983 , 1985, Comput. Vis. Graph. Image Process..

[18]  Li WangDong-Chen He,et al.  Texture classification using texture spectrum , 1990, Pattern Recognit..

[19]  Dani Lischinski,et al.  Solid texture synthesis from 2D exemplars , 2007, SIGGRAPH 2007.

[20]  Manuel G. Penedo,et al.  A fractal-based approach to texture segmentation , 1992 .

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

[22]  Anil K. Jain,et al.  Texture Analysis , 2018, Handbook of Image Processing and Computer Vision.

[23]  Bradley D. Clymer,et al.  Three-dimensional texture analysis of cancellous bone cores evaluated at clinical CT resolutions , 2006, Osteoporosis International.

[24]  Maria Petrou,et al.  Texture anisotropy in 3-D images , 1999, IEEE Trans. Image Process..