This master thesis deals with modern technologies in graphic hardware and using their for general purpose computing. It is primary focused on architecture of unified processors and algorithm implementation via CUDA programming interface. Thesis base is to choose suited algorithm for GPU horsepower demonstration. Main aim of this work is implementation of multiplatform library offering algorithms for discrete volumetric data vectorization. For this purpose was chosen algorithm Marching cubes that is able to find surface of processed object. In created library will be contained algorithm runnable on graphic device and also one runnable on CPU. Finally we compare both variants and discuss their pros and cons.

Generating fully 3D terrains is a dificult task, meaning that we need to store a lot of data or do a lot of computing or both. We can reduce or completly eliminate the data srorage by using a procedural approch, but this is where the problem gets realy computationaly costly and the CUDA platform comes in. CUDA kernels runinng parallely on graphic accelerators can rapidly decrease time needed for computation, allowing even these complex calculations to work in real time or even better. Finding its use in game or movie industry.

This master thesis is focused on volumetric data rendering and on highlighting and visualization of the selected cells of the model organisms. These data are captured by a confocal deconvolution microscope. Input data form one large volumetric block containing separate slices. This data block is rendered by an applicable method and then are identified and visualized the cells marked by the GFP (Green Fluorescent Protein) process or by chlorophyle fluorescency. The principal aim of this work is to find out the preferably optimal effective method enabling this highlighting, most preferably working without a manual check. Due to the data structure, this ambition seems hardly realizable, so it suffices to find out a manual working method. The last step is to embed the results of this work into FluorCam application, the confocal deconvolution microscope data visualizer.