3D Textures Analysis Based on Features Extraction

The Decimal Descriptor Patterns (DDP) operator is a recent method of analyzing textures. Based on structural analysis of pattern distribution, the DDP presents an excellent description of the spatial structure of image texture. In this chapter, two successful applications of this operator are highlighted. The first one consists in Multiple Sclerosis (MS) Lesions detection from 3D Magnetic Resonance (MR) brain images tissue. The second application is 3D face analysis. Different experimentations conducted on a two well-know three-dimensional databases. For comparison, two best known operators of texture measures that are considered as references in image analysis are chosen: the Grey Level Co-occurrence Matrix (GLCM) and the Local Binary Patterns (LBP). Several tests of texture classification are performed in the same conditions using the classifier multiclass Support Vector Machines (SVM). The performance of the three operators is tested in front of illumination, inhomogeneity, contrast, and noise level variations with a various and a large number of textures. Experimental results show clearly the robustness of the DDP operator.