Approaches to mTOR inhibitor texture analysis are usually categorized into structural, statistical, model-based,

and transform methods. Structural approaches Structural approaches6,8 represent texture by well-defined primitives (microtexture) and a hierarchy of spatial arrangements (macrotexture) of those primitives. To describe the texture, one must define the primitives and the placement rules. The choice of a primitive (from a set of primitives) and the probability of the chosen primitive to be placed at a particular location can be a function of location or Inhibitors,research,lifescience,medical the primitives near the location. The advantage of the structural approach is that it provides a good symbolic description of the image; however, this property is more useful for texture synthesis Inhibitors,research,lifescience,medical than analysis tasks. The abstract descriptions can be ill defined for natural textures because of the variability of both micro- and macrostructure and no clear distinction between them. A powerful tool for structural texture analysis is provided by mathematical morphology.9,10 This may prove to be useful for bone image analysis, eg, for the detection of changes in bone microstructure. Statistical Inhibitors,research,lifescience,medical approaches In contrast to structural methods, statistical

approaches do not attempt to explicitly understand the hierarchical structure of the texture. Instead, they represent the texture indirectly by the nondeterministic properties that govern Inhibitors,research,lifescience,medical the distributions and relationships between the gray levels of an image. Methods

based on second-order statistics (ie, statistics given by pairs of pixels) have been shown to achieve higher discrimination rates than the power spectrum (transform-based) and structural methods11. Human texture Inhibitors,research,lifescience,medical discrimination in terms of the statistical properties of texture is investigated in reference 12. Accordingly, the textures in gray-level images are discriminated spontaneously only if they differ in second-order moments. Equal second-order moments, but. different, third-order moments, require deliberate cognitive effort. This may be an indication that, for automatic processing, statistics up to the second order may be the most important.13 The most, popular second-order statistical features for texture analysis are derived from the so-called co-occurrence matrix.8 These have been demonstrated to feature a potential for effective nearly texture discrimination in biomedical images.1,14 Model-based approaches Model-based texture analysis15-20 using fractal and stochastic models attempts to interpret an image texture by use of a generative image model and a stochastic model, respectively. The parameters of the model are estimated and then used for image analysis. In practice, the computational complexity arising in the estimation of stochastic model parameters is the primary problem.