Accurate medical image processing is essential for clinical diagnosis, as it helps physicians identify conditions early and provide timely treatment. Among its components, medical image segmentation is a particularly important step. However, many existing clustering-based segmentation methods treat image enhancement, segmentation, and spatial refinement as separate tasks. This fragmented approach often results in suboptimal segmentation and reduced anatomical consistency. This study addresses this limitation by introducing an integrated hybrid framework for X-ray image enhancement and segmentation. The proposed approach combines adaptive preprocessing with multi–color-space analysis, applies K-means clustering for initial segmentation, uses Fuzzy C-Means (FCM) to model soft class memberships, and incorporates fuzzy connectivity to refine spatial relationships while preserving anatomical continuity. Experiments on real clinical X-ray images show that K-means offers high computational efficiency, while FCM provides better boundary delineation in areas with unclear tissue transitions. Incorporating fuzzy connectivity further improves segmentation performance by reducing fragmentation and strengthening spatial coherence. Overall, the results demonstrate that the proposed hybrid approach outperforms standalone clustering methods, producing more consistent and anatomically meaningful segmentation results. The developed Python-based graphical user interface facilitates interactive visualization and analysis, highlighting the practical applicability of the framework for research, education, and potential clinical decision-support systems.