Anisotropic tubular minimal path model with fast marching front freezing scheme

Li Liu (1) , Da Chen (2, 3, 4) , Laurent D. Cohen (5, 4) , Jiasong Wu (1) , Michel Paques (3) , Huazhong Shu (1)

1 Key Laboratory of Computer Network and Information Integration [Nanjing China]
2 Shandong Artificial Intelligence Institute
3 CHNO - Centre Hospitalier National d'Ophtalmologie des Quinze-Vingts
4 Université Paris Dauphine-PSL
5 CEREMADE - CEntre de REcherches en MAthématiques de la DEcision

In this work, we introduce an anisotropic minimal path model based on a new Riemannian tensor integrating the crossing-adaptive anisotropic radius-lifted tensor field and the front freezing indicator by appearance and path features. The non-local path feature only can be obtained during the geodesic distance computation process by the fast marching method. The predefined criterion derived from path feature is able to steer the front evolution by freezing the point causing high bending of the geodesic to solve the shortcut problem. We performed qualitative and quantitative experiments on synthetic and real images (including retinal vessels, rivers and roads) and compare with the minimal path models with classical anisotropic Riemannian metric and dynamic isotropic metric , which demonstrated the proposed method can detect desired targets from complex tubular tree structures.

Mots clés: Minimal path model; Anisotropy enhancement; Riemannian metric; Path feature; Tubular structures * Corresponding author

Domaines: Traitement des images [eess.IV]; Vision par ordinateur et reconnaissance de formes [cs.CV]; Equations aux dérivées partielles [math.AP]

Li Liu, Da Chen, Laurent D. Cohen, Jiasong Wu, Michel Paques, et al.. Anisotropic tubular minimal path model with fast marching front freezing scheme. Pattern Recognition, 2020, 104, pp.107349. ⟨10.1016/j.patcog.2020.107349⟩. ⟨hal-02996912⟩