Classification of Endoscopic Images Based on Texture Spectrum

This paper suggests a new framework for the discrimination of different texture regions in images using the information that comes from the texture spectrum. We calculate features based on the run lengths of the spectrum image representing the textural descriptors of the respective regions. These measures are used in a classification scheme based on the stability quotient measured between the different regions. This scheme is capable of the characterization among different texture regions within the same image offering a tool for the accurate discrimination among them. The proposed scheme has been successfully applied on different endoscopic images for the right classification between normal and cancer regions. INTRODUCTION A number of methods for the description of the texture have been proposed in the literature (Haralick 1979 – Rao 1990). A common aspect in most of them is the construction of an intermediate formulation, suitable for the description of the distribution of neighboring pixels in the image. Other methods aim to the transformation of the original image in another one, using filtering procedures in order to indicate special texture characteristics of the image. The texture spectrum was initially used as a texture filtering approach and has been introduced in the last few years (He and Wang, 1991). The key concept of this method is the computation of the relative intensity relations between the pixels in a small neighborhood and not on their absolute intensity values. The importance of the texture spectrum method is determined by the extraction of local texture information for each pixel and of the characterization of textural aspect of a digital image in the form of a spectrum. The application of the texture spectrum methodology to a given digital image, resulting to the texture spectrum which characterizes the original image, maintaining the image' s texture characteristics. In the proposed methodology we use the texture spectrum transformation, consisted at first on the extraction of the textural information of neighboring pixels and consequently on the calculation of a set of statistical measures, describing each texture class. The values of these measures form the feature vectors of each texture class, to be used for the discrimination purpose of the proposed classification scheme. With the view to mathematically explain the discriminant results, we proceed to a clustering algorithm, calculating the sum of the average intra-class distance of each class, divided by the product of the average inter-class distance, between a pair of classes (Goldfarb, 1984). The maximization of the distance between the different classes and the minimization of the distance among the features within the same class, at the same time, is the major objective of the proposed methodology. The description of the principles of the method exists in the next section. In section three there is a description of the algorithm of the proposed approach for the calculation of the feature vectors from the texture spectrum and the results of the application of this scheme, as well as the high discrimination ability achieved on endoscopic images, is described in section four. Finally the conclusions and possible extensions of the method are described at the last section. TEXTURE SPECTRUM This section gives a brief description of the principles used for the estimation of the texture spectrum.. The methodology is known in general as a filtering approach of the texture but it will be used here as a preprocessing procedure for the extraction of the textural features. The texture will be faced as an interwoven distribution of the intensities of the pixels. A statistical approach for the description of the texture properties seems more reasonable compared with a structural one (Julesz 1986). A complete definition of the texture spectrum employs the determination of values as the texture unit, the texture unit number and finally the texture spectrum.