A Multifocal RSSeg Approach for Skeletal Age Estimation in an Indian Medicolegal Perspective

Estimating bone age is essential for accurate diagnoses, appropriate care based on biological age, and fairness in legal matters. In the Indian medicolegal context, determining age through a clinical approach involves analyzing multiple joints; however, the traditional method can be tedious and subjective, relying heavily on human expertise, which may lead to biased decisions in age-related legal disputes. Moreover, commonly used radiographs often exhibit pixel-level variations due to heterogeneous contrast, which complicate segmentation tasks and lead to inconsistencies and reduced model performance. The study presents a multifocal region-based symbolic segmentation technique to automatically retain the soft-tissue region that harbors a growth pattern of an ossification center. Experimental results demonstrate an 84.5% Jaccard similarity, an 81.4% Dice coefficient, an 88.3% precision, a 90.0% recall, and a 91.5% pixel accuracy on a novel multifocal dataset of Indian inhabitants. The proposed segmentation technique outperforms U-Net, Attention U-Net, TransU-Net, DeepLabV3+, Adaptive Otsu, and Watershed segmentation in terms of accuracy, indicating strong generalizability across joints and improving reliability. Compared with 86.4% without segmentation, the proposed integration of segmentation with VGG16 classification increases the overall accuracy to 93.8%, demonstrating that target-focused-region processing reduces unnecessary computations and improves feature discrimination without sacrificing accuracy.