Accurate identification of spinal landmarks is critical in postural assessment, spinal deformity monitoring, and biomechanical modeling. Conventional techniques, such as radiographic imaging and manual palpation, suffer from significant limitations, including radiation exposure and operator dependence. This study presents a novel, fully automated method for localizing spinal apophyses using high-resolution 3D surface scans and shape index analysis. The proposed approach extracts the dorsal symmetry line and analyzes perturbations in the shape index, a differential geometric descriptor computed from local principal curvatures, to infer spinous process locations. Experimental validation was conducted on 21 healthy subjects using a structured-light 3D optical scanner, with palpation-based identification serving as a clinical reference. The method demonstrated high sensitivity, with a false negative rate of only 1.58% relative to operator-identified landmarks. The approach also identified additional plausible landmarks not detected by the operator, suggesting potential for improved diagnostic accuracy. These results support the method’s suitability for objective, repeatable spinal landmark detection, with implications for both clinical assessment and computational modeling. Future work will focus on validation against radiographic ground-truth and applicability in pathological cases.