ArticleBMC medical imaging2026
Geometric feature analysis for identifying and characterizing normal variations on chest radiographs.
Article in BMC medical imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeTo develop and evaluate a quantitative method using geometric features of anatomical shadows on chest X-rays (CXRs) to identify normal variant cases and to elucidate the factors characterizing these variations. MATERIALS AND
methodsThis study included 548 normal CXRs confirmed by CT within one month. Shadows of six key anatomical structures (trachea, aorta, right atrium, left ventricle, right/left diaphragm domes) were manually labeled. Geometric features—center of gravity (x, y), length (l), and count (c)—were calculated for each label after image standardization. Cases with features (x, y, l) in the upper/lower 2.5% distribution or with label count (c) ≥ 2 were defined as normal variants. Associated factors were investigated using corresponding CT images and patient age/BMI.
resultsNormal variants were identified in 291/548 cases (53.1%). Analysis of 532 outlier feature instances showed associations with adjacent anatomical structures (41.0%, e.g., vertebrae, pulmonary veins, accessory fissures), spinal curvature/vertebral levels (14.2%, trachea only), and significant correlations with age and/or BMI (44.8%). Age was primarily linked to aortic variations, while BMI correlated more with cardiac and diaphragmatic variations. Notable findings included partial right atrial shadow disappearance near the middle lobe pulmonary vein and a wide normal superior tracheal range (C6-Th2).
conclusionThis quantitative geometric feature method successfully identifies and characterizes a wide range of normal variations on CXRs. It effectively linked feature outliers to specific anatomical structures and patient factors like age and BMI. This approach provides a foundation for automated large-scale analysis, potentially enhancing radiological training and diagnostic accuracy.
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