Learning to Register, Segment and Retrieve Medical Images

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Analysis of medical images by an expert such as a radiologist is an important task for both diagnosis and treatment of many diseases such as cancer. Software programs are regularly used to assist the experts. Three main software operations of medical image analysis are registration (aligning anatomical structures in different images), segmentation (marking the boundaries of an organ or tumour) and retrieval (finding an image in a large database). Registering, segmenting and retrieving medical images are not only very challenging problems (achieving accuracy and efficiency is very difficult for all them), but also they are interrelated. For instance, registration can be used in segmentation, and segmentation can be used for image retrieval. Besides, in most cases, when one of these methods is used, the other two are generally required too. The current technologies neglect this relationship and spend a lot of effort to solve the problems of registration, segmentation and retrieval separately. A unified solution, that considers the relationship between these techniques, is expected to provide more accurate results and save resources. The purpose of this research is to investigate multiple questions. Firstly, we will examine the relationship of theses tasks in medical image analysis more closely, and secondly, we will develop a unified framework that can learn these tasks by characterization of images. In this research, several learning schemes, such as evolving rules and reinforced weights, will be examined to design a new class of algorithms for these three tasks. We will attempt to define a unified framework for all three operations that can autonomously adjust itself to the task at hand by learning from the modality and anatomy specifications. For this purpose, images will be characterized with local and global features as well as with encoding anatomical structures. A unified framework for registering, segmenting and retrieving medical images will increase the efficiency of clinical workflow and help to establish high quality results by incorporating expert feedback. This will have benefits for clinicians (less tedious tasks), hospitals (saving time and resources) and patients (high-quality care).