A dynamic quantum clustering approach to brain tumor segmentation

Introduction Data clustering is a key tool in medical image analysis and classification. Traditional methods such as k -means often require prior knowledge of the number of clusters and may perform poorly in complex, high-dimensional medical datasets. This study develops and evaluates a novel segmentation method based on Dynamic Quantum Clustering (DQC) for brain tumor MRI analysis. Methods The method is first applied to contrast-enhanced T 1 -weighted MRI and compared with conventional algorithms such as k -means. DQC is then extended to multi-modality MRI datasets using two approaches: independent analysis of each modality and joint analysis of fused multi-modality data. Algorithm parameters are optimized to achieve rapid convergence of the clustering dynamics. Results and discussion Results show that, in single-modality MRI, DQC performs comparably or better than k -means while eliminating the need to predefine the number of clusters. In multi-modality applications, DQC provides effective segmentation for both independent and fused datasets. These findings demonstrate that Dynamic Quantum Clustering is a flexible and efficient approach for medical image segmentation, with strong potential for broader applications in medical image analysis.