Abdominal image segmentation and visualization using hierarchical neural networks

Medical imaging modalities can provide very detailed and informative mappings of the anatomy of a subject. Therefore, diagnostic imaging has become an important tool in medicine by increasing knowledge of normal and pathological anatomy, so helping diagnosis and planning treatment. These detailed and informative mappings can be processed to extract the information of interest instead of dealing with whole data. Development of tools and techniques to accomplish information extraction and rendering that information can be grouped under the fields of image segmentation and visualization. These two fields are strongly related with each other and they play a vital role in numerous radiological imaging applications such as the quantification of tissue volumes, diagnosis, localization of pathologies, study of anatomical structures, treatment planning, computer aided surgery and medical education.Segmentation depends highly on the specific application, imaging modality, and other factors such as artifacts, motion, partial volume effects and noise. Imaging of human abdomen is one of the challenging application areas of segmentation due to the highly overlapping intensity ranges of organs of interest. Therefore, selection and development of an appropriate segmentation method depends on the requirements of the problem and organ of interest.On the other hand, the goal of medical visualization is to produce clear and informative pictures of the important structures in a data set but simple approaches have limited performance on visualization of abdomen. Volume visualization can be used either directly with the whole volume data or after a segmentation algorithm. For both cases, volume rendering is an important technique since it displays 3-Dimensional images directly from the original data set and provides "on-the-fly" combinations of the selected image transformations such as opacity and color. The only interactive part during the generation of the volume rendered medical images is the Transfer Function specification, therefore it is important to design effective tools for handling this parameter.For segmentation and visualization tasks discussed above, developing new methods, algorithms, and applications that can be used in medical image segmentation is necessary to use 3-D volume visualization more effectively in diagnosis, treatment planning etc. During the development of these methods, robust and stable query and retrieve from different storage media, ability of manipulating 2-D/3-D images and proper visualization of the results are necessary. Flexible tools and libraries are needed to revisit already-solved problems, to re-develop existing programs, or to rapidly implement and test new algorithms which can save these researchers? time and effort.In this thesis, novel studies on segmentation, interactive visualization of medical images and studies on their implementation are presented. First of all, a robust and patient oriented segmentation algorithm is developed for pre-evaluation of liver transplantation donor candidates. For the, enhancement of the visualization of abdominal organs, a new domain and a technique for multi-stage approximation to this domain, which is then used for transfer function specification for volume rendering, are introduced. Finally, the developed liver segmentation algorithm is implemented as an application of a more general framework on object based medical image segmentation and representation.