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

Year: 2026

Type: article

Abstract: 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).

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.19192329

Author: OmniSciences Research

Institution: Omni Sciences (United States)

Language: English

Topic: Advanced Neuroimaging Techniques and Applications

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

Open Access status: green

OpenAlex ID: W7140096604

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