 Retinal images were acquired on normal and pathological subjects, affected by hypertensive retinopathy of various levels.  The tool has been tested by analyzing a set of 30 arteries, which were previously ordered by a retina specialist by increasing perceived tortuosity.  The tool performances were assessed by computing the value of the Spearman correlation ρ between automatic and manual ordering of the vessel tortuosity.  For the set of 30 analyzed arteries we obtained ρ = A web-based tool for vessel analysis in retinopathy of prematurity D. Fiorin, E. Grisan, A. Ruggeri Department of Information Engineering, University of Padova, Padova, Italy Introduction  Retinopathy of prematurity (ROP) is a disease involving abnormal development of retinal vasculature in premature infants, which can lead to retinal detachment and visual loss.  The evaluation of clinical parameters such as vessel dilation and tortuosity requires frequent, time consuming, and logistically difficult intervention by an ophthalmic expert.  The web-based tool we developed offers a semi-automatic system to perform tracking on vessels and to extract a quantitative assessment of vascular tortuosity and width, to help clinicians in improving the quality of ROP diagnosis. Methods The web-based tool for vessel analysis was developed by taking into account two main purposes: the accessibility, at any time, by users from all over the world, and the user-friendliness of the system. A graphical user interface (GUI), allows the client to intuitively interact with the server-based application that performs analysis on uploaded retinal images. The web-based vessel analysis procedure for the extraction of tortuosity and calibers can be divided into several main steps: uploading an image to the server, vessel selection, server-side image processing and interactive output display. CLIENT SIDE (web browser) SERVER SIDE Login to member area and upload the image to be analyzed Analysis of resulting vessel tortuosity and width Storing image and sending a customized Java GUI to client Processing vessel tracking and computing clinical parametres Sending Java GUI for analysis of results to client The client can choose an image from his/her own drivers and upload it to the server. The only accepted format is jpeg, to reduce the downtime related to data transmission via the web connection. No constrain on image acquisition source or dimensions is present.  Zoom-in and zoom-out functionalities.  Manual identification of optic disk and delimitation of analysis area.  RGB and G-channel visualization options.  Vessel type selection. The vessel is chosen on the image by simply clicking on its end-points; a variable number of additional points is then added to highlight the vessel layout and guide vessel tracking. An interpolating spline, connecting the inserted points, is drawn and displayed over the vessel and updated every time a new point is added by the user. When the whole spline lies inside the vessel, the processing step can be started. A java applet displays the uploaded image and allows the client to set up the tracking process.  The tortuosity index for the vessel is displayed, along with the mean and standard deviation of the vessel caliber.  By moving the mouse cursor over the vessel it is possible to display the vessel widths at different locations.  Possibility to export results to text format and to print the applet panel.  Storing images on the server during the processing time.  Managing multiple clients requests by using sessions variables.  Interacting with clients by means of a Java GUI interface sent to remote browser. Server - side - running php scripts manage important aspects:  A standard tracking (fast), based on a customized version of an algorithm previously proposed, where the starting points (seeds) are replaced by the points manually inserted by the user, and where only the selected vessel is analyzed. A java applet overlays the uploaded image with the tracking results, vessel axis and calibers.  A robust tracking (slower) based on directional filters applied along the direction of the input spline curve that lays over the analyzed vessel. The server loads the uploaded image and the input sent by the client and the processing step starts: The processing includes a tracking step, where vessel axis and edges are detected. Two tracking algorithms are available: Starting from the tracking information, the tortuosity index recently proposed by our group is computed. It is based on partitioning each vessel in segments of constant-sign curvature and then combining together each evaluation of such segments and their number. User friendly vessel selection and other input setting Results Correspondence Diego Fiorin, University of Padova - Dept. of Information Engineering Via Gradenigo 6/a Padova - ITALY ROP images used to develop the system have been provided by the Children’s Hospital of Philadelphia, PA 19104, USA.