This paper introduces a novel framework for localizing region-based active contours, allowing any region-based segmentation energy to be reformulated in a local manner. The approach focuses on evolving contours based on local information rather than global image statistics, making it capable of segmenting objects with heterogeneous feature profiles. The framework is versatile and can be applied to various global region-based active contour energies, improving their performance in certain scenarios. The authors describe the framework and demonstrate its application to three well-known energies: uniform modeling, means separation, and histogram separation. They compare the localized energies to their global counterparts, showing significant improvements in accuracy. The paper also explores the effects of the localization radius on segmentation results and provides guidelines for choosing the appropriate radius. Additionally, the authors extend the technique to segment multiple objects simultaneously and discuss implementation details, including the computational complexity and convergence properties. Experiments on challenging images validate the robustness and accuracy of the proposed method.This paper introduces a novel framework for localizing region-based active contours, allowing any region-based segmentation energy to be reformulated in a local manner. The approach focuses on evolving contours based on local information rather than global image statistics, making it capable of segmenting objects with heterogeneous feature profiles. The framework is versatile and can be applied to various global region-based active contour energies, improving their performance in certain scenarios. The authors describe the framework and demonstrate its application to three well-known energies: uniform modeling, means separation, and histogram separation. They compare the localized energies to their global counterparts, showing significant improvements in accuracy. The paper also explores the effects of the localization radius on segmentation results and provides guidelines for choosing the appropriate radius. Additionally, the authors extend the technique to segment multiple objects simultaneously and discuss implementation details, including the computational complexity and convergence properties. Experiments on challenging images validate the robustness and accuracy of the proposed method.