PRIOR KNOWLEDGE FOR TARGETED OBJECT SEGMENTATION IN MEDICAL IMAGES
Medical image segmentation (MIS), the task of partitioning an image into meaningful parts, is an important step toward automating medical image analysis and is at the crux of a variety of medical imaging applications, such as computer aided diagnosis, therapy planning and delivery, and computer aided interventions. However, existence of noise, low contrast and objects' complexity in medical images preclude ideal segmentation. Incorporating prior knowledge into image segmentation algorithms has proven useful for obtaining more accurate and plausible results on targeted objects segmentation.
In the proposed thesis, we develop novel techniques to augment optimization-based segmentation frameworks with different types of prior knowledge to identify and delineate only those objects (targeted objects) that conform to specific geometrical, topological and appearance priors. These techniques fall into two classes. The first class involves employing prior knowledge to segment multi-part objects with part-configuration constraints and the second class involves encoding priors based on images acquired from different imaging equipments and of differing dimensions.
Ph.D. Examining Committee:
Dr. Ghassan Hamarneh, Senior Supervisor
Dr. Greg Mori, Supervisor
See a list of his publications here: