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Scientific visualization evolved up to the stage where collaborative work in virtual coexistence of various experts has become unavoidable in identifying and solving specific visualization tasks. These problems encouraged us to research and define key aspects of visualization service which will enable collaborative work over the Internet. In this paper we define architecture of the whole system and unified interface for all requirements in such environment. Unified requests are sent to visualization processing unit to execute required visualization routine. Collaborative work on the volumetric medical data is a specially demanding task, so we present development of VMI (Visualization of Medical data over the Internet) system, where segmentation for visualization is based on joint histogram.

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This paper deals with surface reconstruction and the gradient reconstruction in the volume rendering. In the volume rendering procedure, reconstruction according to the discrete set of samples is required. Due to the reconstruction procedure alias artifacts, in the final image, could not be neglected. In this paper we focus our attention on the gradient reconstruction based on the surface reconstruction. For the surface reconstruction we use the cubic B-splines and for the gradient reconstruction corresponding derivative.If the noise is present in the input data the approximation I)-spline is used, and the interpolation B-spline is used for the input signal without the noise. The shading procedure requires normal estimation. Two approaches are used for normal estimation. The classic approach for normal estimation is central difference calculation, and we propose derivative calculation of the reconstruction B-spline function. We show that calculation of the normal vector has important influence on the alias artifacts in the result.