This work concerns / deals with theoretical analysis, design and implementation of system for creation three-dimensional computer models of human body tissues. These models will be created by computer tomography (CT) and magnetic resonance (MR) data. Output of medical apparatuses based on these principles is a three-dimensional raster, which is not good for visualization and making real models. For required tissues extraction are used automatic or semiautomatic methods, where is necessary an operation of a specialist. This work is concerned with human body tissues with linear structure such as veins and arteries, nerves, bone canals etc. These structures are problematic recognizable by automatic methods. Therefore is convenient an operation of a specialist, which is adept in human anatomy.

In radiation therapy, it is imperative to deliver high doses of radiation to the tumor while reducing radiation to the healthy tissue. Respiratory motion is the most significant source of errors during treatment. Therefore, it is essential to accurately model respiratory motion for precise and effective radiation delivery. Many approaches exist to account for respiratory motion, such as controlled breath hold and respiratory gating, and they have been relatively successful. They still present many drawbacks. Thus, research has been expanded to tumor tracking. The overall goal of 4D-CT is to predict tumor motion in real time, and this work attempts to move in that direction. The following work addresses both the temporal and the spatial aspects of four-dimensional CT reconstruction. The aims of the paper are to (1) estimate the temporal parameters of 4D models for anatomy deformation using a novel neural network approach and (2) to use intelligently chosen non-uniform, non-separable splines to improve the spatial resolution of the deformation models in image registration.