Comparative Evaluation of Hard and Soft Clustering for Precise Brain Tumor Segmentation in MR Imaging
This work addresses the need for accurate tumor segmentation in medical imaging for clinical applications, but it is incremental as it evaluates existing methods without introducing new techniques.
The study compared hard clustering (K-Means) and soft clustering (Fuzzy C-Means) for brain tumor segmentation in MRI, finding that K-Means was faster at 0.3s per image, while FCM achieved higher accuracy with a Dice score of 0.67 versus 0.43 for K-Means.
Segmentation of brain tumors from Magnetic Resonance Imaging (MRI) remains a pivotal challenge in medical image analysis due to the heterogeneous nature of tumor morphology and intensity distributions. Accurate delineation of tumor boundaries is critical for clinical decision-making, radiotherapy planning, and longitudinal disease monitoring. In this study, we perform a comprehensive comparative analysis of two major clustering paradigms applied in MRI tumor segmentation: hard clustering, exemplified by the K-Means algorithm, and soft clustering, represented by Fuzzy C-Means (FCM). While K-Means assigns each pixel strictly to a single cluster, FCM introduces partial memberships, meaning each pixel can belong to multiple clusters with varying degrees of association. Experimental validation was performed using the BraTS2020 dataset, incorporating pre-processing through Gaussian filtering and Contrast Limited Adaptive Histogram Equalization (CLAHE). Evaluation metrics included the Dice Similarity Coefficient (DSC) and processing time, which collectively demonstrated that K-Means achieved superior speed with an average runtime of 0.3s per image, whereas FCM attained higher segmentation accuracy with an average DSC of 0.67 compared to 0.43 for K-Means, albeit at a higher computational cost (1.3s per image). These results highlight the inherent trade-off between computational efficiency and boundary precision.