Improving information quality of MR brain images by fully automatic and robust image analysis methods

Abstract

Abstract— The medical experts are expected to make diagnosis on ever‐increasing amount of data. Although the intention with the technological advances in imaging devices, such as MRI (magnetic resonance imaging), is to improve the medical practice, the increased amount of data may make the medical expert's task even harder and could arguably be counter‐productive. A more desirable situation for the medical expert is the improvement in the clinical relevance of the image content and a faster, preferably automated, access to the most relevant part of the existing large amount of data. To this effect, the concept of information quality is introduced, and fully automatic, fast, and robust image analysis methods that improve it are proposed. As a guideline for a general framework, these methods are classified by the type of information they can improve: geometric, structural (sub‐organ), and organizational. The algorithmic contributions are in the domain of MR brain image analysis. These methods improve the information quality not only for the visualization and the diagnosis by the expert, but also for the quantification by other automated methods. The robustness of these algorithms has been extensively tested at the Leiden University Medical Center over a large database of 550 patients.