Abstract
The most common primary malignant brain tumor is glioblastoma. Glioblastoma Multiforme (GBM) diagnosis is difficult. However, image segmentation and registration methods may simplify and automate Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) scan analysis. Medical practitioners and researchers can better identify and characterize glioblastoma tumors using this technology. Many segmentation and registration approaches have been proposed recently. Note that these approaches are not fully compiled. This review efficiently and critically evaluates the state-of-the-art segmentation and registration techniques for MRI and CT GBM images, providing researchers, medical professionals, and students with a wealth of knowledge to advance GBM imaging and inform decision-making. GBM's origins and development have been examined, along with medical imaging methods used to diagnose tumors. Image segmentation and registration were examined, showing their importance in this difficult task. Frequently encountered glioblastoma segmentation and registration issues were examined. Based on these theoretical foundations, recent image segmentation and registration advances were critically analyzed. Additionally, evaluation measures for analytical efforts were thoroughly reviewed.
Keywords: Glioblastoma, MRI, CT, Image segmentation, Image registration, Malignant brain tumor.
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