In this thesis several methods of volume segmentation have been described, and also their application to the specific problem of constructing a three-dimensional model of a selected object from medical CT images of the human body. The thesis also describes the process of formation of medical images in DICOM format which are used as input data, and their shaping for further processing. A region of interest which includes the object of segmentation is selected from the series of 2D images that represent axial cross-sections through the body. The object of segmentation are several vertebrae. A 3D discrete grid is formed from the region of interest, considering the real-world dimensions which are stored in DICOM metadata. Three methods for 3D modeling are implemented: amplitude thresholding based on finding the regions with values above a threshold, and two methods based on finding a boundary surface (developed from methods for edge detection on a 2D image) --- Canny algorithm segmentation, and segmentation based on local phase coherence. As there is no physical referent model for selected object of segmentation, for every method a number of models has been formed by variating the algorithm parameters. Using those models, averaging has been conducted in order to find an estimation of model with minimal dispersion.