Over the last few years, there has been an increase in the clinical methods targeting the human tracheobronchial tree, both for therapeutic and diagnostic purposes. For these methods to be effective, a good understanding of the lung structure is necessary. This knowledge can be attained through the use of medical imaging protocols such as CT and MRI, and can in turn be used to predict aerosol deposition for particles employed for inhalation therapy via the simultaneous use of radionuclide imaging. However, due to limitations imposed by the technologies currently available, not enough information can be gathered in-vivo about the respiratory tract. Consequently, widespread use of anatomical models of the lung is being made by clinicians in order to enable them to fill this gap in information. The thesis is concerned with the improvement of such models and the introduction of new, more advanced ones in an effort to accurately describe the human lung using mathematical and physical principles. A method is developed for improving the Conceptual Model constructed in the Nuclear Medicine Department of Southampton General Hospital by incorporating to it real, patient-specific data obtained through CT imaging. A model of the bronchopulmonary segments of the lung is also created and an atlas that can be used for the identification of these sub-structures in any lung space is formed. An algorithm for the generation of a fully-descriptive 3D model of the airway tree is then designed and implemented, the morphometry of which is assessed to confirm that it is a realistic representation of the target organ. The deterministic algorithm reveals the 3D geometry and orientation of the lung airways, thus enabling aerosol deposition and flow-pattern studies to be performed in a comprehensive way in previously inaccessible regions of the lung.