The present work applies Gabor filters bank for texture analysis of skin lesions images, obtained by ultrasound biomicroscopy. The regions affected by the lesions were differentiated from surrounding tissue in all the analyzed cases; however the accuracy of the traced borders showed some limitations in part of the images. Future steps are being contemplated, attempting to enhance the technique performance.
Since the upturn of intravascular ultrasound (IVUS) as an imaging technique for the coronary artery system, much research has been done to simplify the complicated analysis of the resulting images. In this study, an attempt to develop an automatic tissue characterization algorithm for IVUS images was done. The first step was the extraction of texture features. The resulting feature space was used for classification, constructing a likelihood map to represent different coronary plaques. The information in this map was organized using a recently developed [1] geodesic snake formulation, the so-called Stop & Go snake. The novelty of our study lies in this last step, as it was the first time to apply the Stop & Go snake to segment IVUS images.
We prove that groups acting geometrically on delta-quasiconvex spaces contain no essential Baumslag-Solitar quotients as subgroups. This implies that they are translation discrete, meaning that the translation numbers of their nontorsion elements are bounded away from zero.