Abstract

This study proposes the novel dilated divergence scale-space representation for multidimensional curve-like image structure analysis. In the proposed framework, image structures are modeled as curves with arbitrary thickness. The dilated divergence analyzes the structure bound- aries along the curve normal space in a multi-scale fashion. The dilated divergence based detection is formulated so as to 1) sustain the distur- bance introduced by neighboring ob jects, 2) recognize the curve normal and tangent spaces. The latter enables the innovative formulation of structure eccentricity analysis and curve tangent space-based structure motion analysis, which have been scarcely investigated in literature. The proposed method is validated using 2D, 3D and 4D images. The struc- ture principal direction estimation accuracies, structure scale detection accuracies and detection stabilities are quantified and compared against two scale-space approaches, showing a competitive performance of the proposed approach, under the disturbance introduced by image noise and neighboring ob jects. Moreover, as an application example employ- ing the dilated divergence detection responses, an automated approach is tailored for spinal cord centerline extraction. The proposed method is shown to be versatile to well suit a wide range of applications.