Geometric Feature Extraction for Diffusion Tensor Classification: Benchmark Results on Stanford HARDI

OmniSciences Technical Report TR-2026-002 We present benchmark results for proprietary geometric feature extraction applied to diffusion tensor imaging (DTI) tissue classification. The method operates on 3×3 symmetric positive-definite diffusion tensors on the Riemannian symmetric space GL+(3)/SO(3). Key results on Stanford HARDI (4-class): 76.44% accuracy with geometric features vs 69.22% with raw tensors (+7.2pp) Curvature Anisotropy (CA) metric detects fiber crossings where FA fails Cross-domain validation: same framework achieves 96.73% on PolSAR Patent-pending (U.S. Provisional Application No. 64/011,831).