A Fully Automated Approach to Segmentation and Registration of Medical Image Data for Pulmonary Diagnosis

Abstract

<p>Molecular imaging is an exciting and relatively new technology that has found widespread use in the diagnosis and observation of various diseases. More recently, molecular imaging has penetrated areas such as drug development in order to facilitate the observation and analysis of the effects of newly developed drugs. The amounts of data in drug development experiments may be very large due to the fact that they contain both spatial and temporal information of medical images. Imaging techniques can facilitate the analysis of large amounts of data by automating information extraction and providing meaningful results.</p> <p>The focus of the project concerning this thesis is to create a emporal and spatial atlas of an animal by utilizing and integrating data from images of different modalities. More specifically, the application treated in the thesis makes use of ventilation and perfusion data from CT and SPECT scans in order to aid in the observation of the effects of newly developed drugs in the treatment of lung diseases. This thesis describes the segmentation and registration techniques used in detail and how these were utilized to align and combine ventilation and perfusion data from both CT and SPECT scans.</p>