Application Of Fractal Texture Analysis To Segmentation Of Dental Radiographs

Abstract This work discusses the development of an image segmentation algorithm that utilizes fractional Brownian motion as a fractal texture model. Additionally a second feature, the correlation coefficient of the regression analysis associated with the fractal dimension computation, is also used to assess which regions of the radiograph are well described by the fractional Brownian motion model and hence, are more likely to be bone. The resulting three dimension feature space consisting of the local fractal dimension, radiograph intensities and regression analysis correlation coefficient waspartitioned to identify each pixel of the radiographs as being either bone, teeth, or a boundary between these entities. The results obtained in this study indicate the potential utility of a textural based algorithm for segmentation of dental radiographs. 1.0 IntroductionThe detection and monitoring of certain periodontal diseases, which are typically manifested via bone loss that occurs over a period of months