Second Order Kinematic Surface Fitting in Anatomical Structures
This work addresses the need for better symmetry detection and classification in medical image analysis, particularly for complex anatomical structures, though it appears incremental as it builds on existing kinematic surface fitting methods.
The paper tackled the problem of accurately capturing the intricate curved and twisted nature of anatomical structures in symmetry detection and morphological classification by proposing a second order velocity field for kinematic surface fitting, which improved accuracy and enabled detection of curved rotational symmetries and classification of shapes like human cochleae.
Symmetry detection and morphological classification of anatomical structures play pivotal roles in medical image analysis. The application of kinematic surface fitting, a method for characterizing shapes through parametric stationary velocity fields, has shown promising results in computer vision and computer-aided design. However, existing research has predominantly focused on first order rotational velocity fields, which may not adequately capture the intricate curved and twisted nature of anatomical structures. To address this limitation, we propose an innovative approach utilizing a second order velocity field for kinematic surface fitting. This advancement accommodates higher rotational shape complexity and improves the accuracy of symmetry detection in anatomical structures. We introduce a robust fitting technique and validate its performance through testing on synthetic shapes and real anatomical structures. Our method not only enables the detection of curved rotational symmetries (core lines) but also facilitates morphological classification by deriving intrinsic shape parameters related to curvature and torsion. We illustrate the usefulness of our technique by categorizing the shape of human cochleae in terms of the intrinsic velocity field parameters. The results showcase the potential of our method as a valuable tool for medical image analysis, contributing to the assessment of complex anatomical shapes.