Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
534240 | Pattern Recognition Letters | 2011 | 10 Pages |
We propose a purely discrete deformable partition model for segmenting 3D images. Its main ability is to maintain the topology of the partition during the minimization process. To do so, our main contribution is a new definition of multi-label simple points (ML simple point) that is easily computable. An ML simple point can be relabeled without modifying the overall topology of the partition. The definition is based on intervoxel properties, and uses the notion of collapse on cubical complexes. This work is an extension of a former restricted definition (Dupas et al., 2009) that prohibits the move of intersections of boundary surfaces. A deformation process is carried out with a greedy energy minimization algorithm. A discrete area estimator is used to approach at best standard regularizers classically used in continuous energy minimizing methods. We illustrate the potential of our approach with the segmentation of 3D medical images with known expected topology.
Graphical abstractFigure optionsDownload full-size imageDownload high-quality image (67 K)Download as PowerPoint slideResearch highlights► Definition of 3D simple points for multi-label images. ► Use these ML-simple points to modify a 3D partition while preserving its topology. ► Propose a 3D discrete deformable partition model. ► Use this deformable partition model for 3D image segmentation.