RESUMEN
We propose an algorithm for vessel extraction in retinal images. The first step consists of applying anisotropic diffusion filtering in the initial vessel network in order to restore disconnected vessel lines and eliminate noisy lines. In the second step, a multiscale line-tracking procedure allows detecting all vessels having similar dimensions at a chosen scale. Computing the individual image maps requires different steps. First, a number of points are preselected using the eigenvalues of the Hessian matrix. These points are expected to be near to a vessel axis. Then, for each preselected point, the response map is computed from gradient information of the image at the current scale. Finally, the multiscale image map is derived after combining the individual image maps at different scales (sizes). Two publicly available datasets have been used to test the performance of the suggested method. The main dataset is the STARE project's dataset and the second one is the DRIVE dataset. The experimental results, applied on the STARE dataset, show a maximum accuracy average of around 94.02%. Also, when performed on the DRIVE database, the maximum accuracy average reaches 91.55%.
RESUMEN
PURPOSE: Spontaneous blood echogenicity in vein ultrasound images may be a marker for an increased erythrocyte aggregability, but a reliable quantitative evaluation method is a prerequisite for its use in clinical studies. We compared a simple scoring system of blood echogenicity intensity and pattern, with automatic image analysis. MATERIAL AND METHODS: 157 femoral and popliteal vein digitized ultrasound sequences were reviewed by two independent observers who chose an image, delimited an area of interest (ROI), and graded blood echogenicity intensity and pattern, using a four class score. Each observer reviewed the images selected by the other, without and with ROI. The computer calculated first and second order parameters describing echo intensity and spatial organization. RESULTS: Inter-observer reproducibility of subjective assessment was poor (Kappa<0.5), whereas the automatically calculated ROI average gray level intensity relatively to the whole image (tau(1)) effectively separated all grades of intensity. No parameter effectively separated patterns. CONCLUSION: Tau(1) is a simple parameter for the in vivo evaluation of blood echogenicity intensity. It should be evaluated in standardized conditions for clinical hemorheology studies in correlation with in vitro erythrocyte aggregation measurements.