Second-Order Kinetic Approach for Anatomical Shape Analysis and Symmetry-Based Core-Line Detection

Ismaila IO and Lawal AA

Published on: 2026-02-17

Abstract

Anatomical shape analysis and symmetry detection are fundamental to biomedical image interpretation, supporting diagnostics, disease progression monitoring, and surgical planning. However, traditional geometric models often fail to preserve anatomical continuity and structural fidelity when confronted with complex deformable biological forms. This research introduces a novel framework grounded in second-order kinetic theory to examine anatomical shape, extract symmetry properties, and identify core lines that serve as structural skeletons of organs and tissues.

The model employs second-order differential operators that capture both inertial and diffusive aspects of anatomical form evolution, enabling robust core-line detection under varying imaging conditions. Through the integration of partial differential equations (PDEs) and computational symmetry constraints, the framework provides a mathematically consistent approach for examining shape smoothness, curvature stability, and intrinsic symmetry.

Experimental validation on synthetic and real biomedical datasets demonstrates superior accuracy and continuity in core-line extraction compared to classical level-set and morphological skeletonization methods. The proposed approach establishes a bridge between kinetic modeling, biomedical shape analysis, and computational anatomy, offering a promising direction for advanced diagnostic and morphometric systems.