Segmentation techniques for noisy MRI scans

Abstract

Every year, 180000 new cases of hydrocephalus are diagnosed among infants in Sub-Saharan Africa. Unfortunately, more than two-thirds of the population in this region lacks access to essential medical imaging technologies, such as magnetic resonance imaging (MRI). To address this issue, a collaborative effort between the TU Delft, Leiden University Medical Center, Penn State, and Mbarara University of Science and Technology has led to the development of a low-cost, portable, low-field MRI system. However, images obtained from this scanner are often noisy and distorted and might contain artefacts, therefore, need preprocessing before they can be utilized in diagnostics. The enhancement of their quality can be achieved through both hardware calibration and optimization, as well as the application of filtering, enhancement, and segmentation techniques. In this master's project, we propose a two-step PDE-based segmentation approach. Additionally, we compare it with the modified approach where presegmentation in the initial phase of the standard algorithm is introduced. Both approaches yield segmentation results comparable to the ground truth or manually performed segmentation. Nonetheless, there remains room for further improvement in both denoising and segmentation techniques.