It can be used also for texture Transform methods Transform methods of texture analysis, such as Fourier24-26 and wavelet27-29 transforms, produce an image in a space whose coordinate system has an interpretation that is closely related to the characteristics of a texture (such as frequency or size). Methods based on the Fourier transform perform poorly in practice, due to lack of spatial localization. Gabor filters provide means for better spatial localization; however, their usefulness is limited in practice because there is usually no single filter resolution at which one can localize a spatial structure in natural textures. Compared

with the Gabor transform, the wavelet transform have several advantages: Varying the spatial Inhibitors,research,lifescience,medical resolution allows it to represent textures at. the most, appropriate scale. There is a wide range of choices for the wavelet function, and so the best-suited wavelets for texture analysis can be chosen a specific application. Wavelet transform Inhibitors,research,lifescience,medical is thus attractive for texture segmentation. The problem with wavelet transform is that it. is not translation-invariant.30,31 Regardless of their definition and underlying approach to texture analysis, texture features should allow good discrimination between texture classes, show weak mutual correlation, Inhibitors,research,lifescience,medical preferably allow linear class separability, and demonstrate good correlation with physical structure Inhibitors,research,lifescience,medical properties.

For a more detailed review of basic techniques of quantitative texture analysis, the reader is referred to reference 2. In this paper, we will discuss practical implementation of these techniques, in the form of MaZda computer program. MaZda: a software package for quantitative texture analysis The main steps of the intended image texture analysis are illustrated

Inhibitors,research,lifescience,medical in Figure 3. First, the image is acquired by means of a suitable scanner. The ROIs are defined using the interactive graphics user selleck kinase inhibitor interface of the MaZda program. (The name “MaZda” is an acronym derived from “Macierz Zdarzen,’ which is Polish for ”co-occurrence matrix.“ Thus, MaZda has no connection with Oxygenase the Japanese car manufacturer.) Up to 16 ROIs can be defined for an image; they may overlap each other. Once ROIs are established, MaZda allows calculation of texture parameters available from a list of about 300 different definitions that cover most of the features proposed in the known literature. The parameters can be stored in text files. Figure 3. Main steps of digital image texture analysis. One can demonstrate using properly designed test images that some of the higher-order texture parameters, especially those derived from the co-occurrence matrix, show correlation to first-order parameters, such as the image mean or variance. To avoid this unwanted phenomenon, prior to feature extraction, image normalization is preferably performed. Typically, the features computed by MaZda are mutually correlated.