A Robust Rule-Based Framework for Stone Detection and Posterior Acoustic Shadow Localization in Abdominal Ultrasound.

Posterior acoustic shadowing is a fundamental physical phenomenon associated with calcified stones in ultrasound image, yet it has not been fully exploited in automated ultrasound analysis. This study aimed to develop an explainable, semi-automatic rule-based framework that explicitly incorporates posterior acoustic shadow characteristics for stone detection and localization in a clinically guided manner. A rule-based framework was designed to generate stone candidates using morphological enhancement and to evaluate them through local contrast analysis, posterior shadow region assessment, and shape-based penalties. A composite score integrating these features was used to rank candidates. The method was evaluated on 52 kidney stone and 66 gallbladder stone ultrasound images, stratified into three diagnostic confidence categories. Performance was assessed using an ablation study and centroid distance error measured in pixels relative to expert-defined references. In the 50-60% confidence group, the accuracy increased from 0.29 to 0.64 for kidney stones and from 0.30 to 0.60 for gallbladder stones when posterior shadow information was included. Centroid distance errors in the ≥80% confidence group were 1.26 ± 0.28 mm for kidney stones and 1.44 ± 0.91 mm for gallbladder stones. The proposed framework enhances diagnostic confidence by leveraging physically grounded posterior acoustic shadow analysis and provides a reproducible augmentation to conventional ultrasound-based stone assessment.