This paper presents a deterministic, multi-scale method for extracting regions of interest (ROIs) based exclusively on intrinsic structural information, without relying on semantic priors, object detection models, or learned representations. The proposed approach builds upon a dense structural map (Φ), computed locally over sliding windows, and introduces a multi-scale consensus mechanism to enhance robustness against scale selection bias. Instead of depending on a single analysis window, structural activations are evaluated across multiple spatial scales (ws ∈ {5, 7, 9, 11}), and a majority voting strategy is applied to retain only scale-persistent structures. This process filters unstable activations while preserving information-coherent regions. The method is fully deterministic and interpretable. It does not require training data, semantic labeling, or domain adaptation. ROI extraction is performed through structural thresholding, non-maximum suppression, and spatial constraints, with explicit control over maximum ROI count and coverage. Extensive evaluation is conducted on 150 natural images. The study quantifies: ROI coverage and spatial reduction rates Structural gain inside and outside ROI (ΔGuideIn / ΔGuideOut) Inter-scale stability via non-parametric statistical tests (Wilcoxon signed-rank, Friedman test) Kendall’s W coefficients to assess consistency across window sizes Results show that the multi-scale consensus significantly reduces sensitivity to window size selection while producing more stable and coherent ROIs. Although the multi-scale configuration increases overall ROI coverage compared to the most selective mono-scale setting, it improves structural persistence and reduces fragmentation effects. The proposed framework establishes a scale-robust structural foundation for downstream image processing tasks such as selective enhancement and structure-constrained super-resolution. It demonstrates that reliable ROI extraction can be achieved through structural coherence alone, without semantic modeling.