Improving Segmentation of MRI Images of Brain Glial Cells Using Fuzzy C-Means Algorithm and Combination of PET Images

Positron emission tomography (PET) imaging is an essential diagnostic tool in the detection and management of brain disorders, offering unparalleled precision in identifying and differentiating between normal and abnormal soft tissues. Brain gliomas, one of the most common forms of brain tumors, present significant challenges for clinicians due to their variability in shape, size, and behavior. Accurate segmentation of these tumors is critical for diagnosis and treatment planning but is often hindered by the limitations of manual methods, which are both time-intensive and prone to human error. This study introduces an advanced automated segmentation method that integrates PET imaging with magnetic resonance imaging (MRI) data to improve the accuracy and efficiency of glioma segmentation. The Fuzzy C-means (FCM) algorithm is employed to cluster MRI images, which are then enhanced by applying PET-derived masks. By leveraging PET intensity patterns to categorize specific regions, the method addresses ambiguities in tumor boundaries and improves delineation precision. The integration of PET and MRI data in this hybrid approach significantly enhances segmentation accuracy, as confirmed by experimental results. The proposed method demonstrates improved performance compared to traditional segmentation techniques, providing a robust tool for clinicians to make more informed decisions in diagnosing and planning treatments for gliomas. This research highlights the potential of combining multimodal imaging data with advanced computational algorithms to streamline medical imaging workflows. By reducing the reliance on manual methods, this approach not only saves time but also ensures greater reliability in diagnosing and managing brain tumors, thereby improving patient outcomes.