The overall aim of my PhD research work is to design, develop, and evaluate a new practical environment to generate separated representations of arterial and venous trees in non-contrast pulmonary CT imaging of human subjects and to extract quantitative measures at different tree-levels. Artery/vein (A/V) separation is of substantial importance contributing to our understanding of pulmonary structure and function, and immediate clinical applications exist, e.g., for assessment of pulmonary emboli. Separated A/V trees may also significantly boost performance of airway segmentation methods for higher tree generations. Although, non-contrast pulmonary CT imaging successfully captures higher tree generations of vasculature, A/V are indistinguishable by their intensity values, and often, there is no trace of intensity variation at locations of fused arteries and veins. Patient-specific structural abnormalities of vascular trees further complicate the task. We developed a novel multi-scale topo-morphologic opening algorithm to separate A/V trees in non-contrast CT images. The algorithm combines fuzzy distance transform, a morphologic feature, with a topologic connectivity and a new morphological reconstruction step to iteratively open multi-scale fusions starting at large scales and progressing towards smaller scales. The algorithm has been successfully applied on fuzzy vessel segmentation results using interactive seed selection via an efficient graphical user interface developed as a part of my PhD project. Accuracy, reproducibility and efficiency of the system are quantitatively evaluated using computer-generated and physical phantoms along with in vivo animal and human data sets and the experimental results formed are quite encouraging. Also, we developed an arc-skeleton based volumetric tree generation algorithm to generate multi-level volumetric tree representation of isolated arterial/venous tree and to extract vascular measurements at different tree levels. The method has been applied on several computer generated phantoms and CT images of pulmonary vessel cast and in vivo pulmonary CT images of a pig at different airway pressure. Experimental results have shown that the method is quite accurate and reproducible. Finally, we developed a new pulmonary vessel segmentation algorithm, i.e., a new anisotropic constrained region growing method that encourages axial region growing while arresting cross-structure leaking. The region growing is locally controlled by tensor scale and structure scale and anisotropy. The method has been successfully applied on several non-contrast pulmonary CT images of human subjects. The accuracy of the new method has been evaluated using manually selection of vascular and non-vascular voxels and the results found are very promising.