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A texture is a pattern represented on a surface or a structure of an object. Classifying a texture involves pattern recognition. Integration of texture classification is a one step ahead for machine vision application. For the past few decades until now, many researchers from various fields have been trying to develop algorithms to do texture discrimination. Alfred Haar created the first discrete wavelet transform (DWT) in Haar (1911) which led to the development of the fast Fourier transform (FFT) and the other forms of DWT to detect periodic signals. Haralick had developed grey level co-occurrence probabilities (GLCP) with statistical features to describe textures (Haralick et al., 1973). The hidden Markov model (HMM) is another statistical method used for pattern recognition (Rabiner, 1989).

A Design for Stochastic Texture Classification Methods in Mammography Calcification Detection