Literature DB >> 21078578

Geometrically induced force interaction for three-dimensional deformable models.

Si Yong Yeo1, Xianghua Xie, Igor Sazonov, Perumal Nithiarasu.   

Abstract

In this paper, we propose a novel 3-D deformable model that is based upon a geometrically induced external force field which can be conveniently generalized to arbitrary dimensions. This external force field is based upon hypothesized interactions between the relative geometries of the deformable model and the object boundary characterized by image gradient. The evolution of the deformable model is solved using the level set method so that topological changes are handled automatically. The relative geometrical configurations between the deformable model and the object boundaries contribute to a dynamic vector force field that changes accordingly as the deformable model evolves. The geometrically induced dynamic interaction force has been shown to greatly improve the deformable model performance in acquiring complex geometries and highly concave boundaries, and it gives the deformable model a high invariancy in initialization configurations. The voxel interactions across the whole image domain provide a global view of the object boundary representation, giving the external force a long attraction range. The bidirectionality of the external force field allows the new deformable model to deal with arbitrary cross-boundary initializations, and facilitates the handling of weak edges and broken boundaries. In addition, we show that by enhancing the geometrical interaction field with a nonlocal edge-preserving algorithm, the new deformable model can effectively overcome image noise. We provide a comparative study on the segmentation of various geometries with different topologies from both synthetic and real images, and show that the proposed method achieves significant improvements against existing image gradient techniques.

Mesh:

Year:  2010        PMID: 21078578     DOI: 10.1109/TIP.2010.2092434

Source DB:  PubMed          Journal:  IEEE Trans Image Process        ISSN: 1057-7149            Impact factor:   10.856


  2 in total

1.  Segmentation of biomedical images using active contour model with robust image feature and shape prior.

Authors:  Si Yong Yeo; Xianghua Xie; Igor Sazonov; Perumal Nithiarasu
Journal:  Int J Numer Method Biomed Eng       Date:  2013-10-28       Impact factor: 2.747

2.  User Interaction in Semi-Automatic Segmentation of Organs at Risk: a Case Study in Radiotherapy.

Authors:  Anjana Ramkumar; Jose Dolz; Hortense A Kirisli; Sonja Adebahr; Tanja Schimek-Jasch; Ursula Nestle; Laurent Massoptier; Edit Varga; Pieter Jan Stappers; Wiro J Niessen; Yu Song
Journal:  J Digit Imaging       Date:  2016-04       Impact factor: 4.056

  2 in total

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