Level set segmentation from multiple non-uniform volume datasets

Abstract

Figure 1: 3D level set model of a griffin derived from two non-uniform laser scan reconstruction volume datasets. The two input models with resolution 294 x 312 x 24 and 294x 52 x 144 show severe aliasing effects from insufficient sampling. especially in the wings. Our method merges information from both of the datasets to produce a high resolution 294x312x 144 level set model. All models throughout this paper are flat-shaded to highlight details. ABSTRACTTypically 3-D MR and CT scans have a relatively high resolution in the scanning X -Y plane, but much lower resolution in the axial Z direction. This non-uniform sampling of an object can miss small or thin structures. One way to address this problem is to scan the same object from multiple directions. In this paper we describe a method for deforming a level set model using velocity information derived from multiple volume datasets with non-uniform resolution in order to produce a single high-resolution 3D model. The method locally approximates the values of the multiple datasets by fitting a distance-weighted polynomial using moving least-squares. The proposed method has several advantageous properties: its compu tational cost is proportional to the object surface area, it is stable with respect to noise, imperfect registrations and abrupt changes in the data, it provides gain-correction, and it employs a distance-' based weighting to ensures that the contributions from each scan are properly merged into the final result. We have demonstrated the effectiveness of our approach on four multi-scan datasets, a griffin laser scan reconstruction, a CT scan of a teapot and MR scans of a mouse embryo and a zucchini.

Algorithms for Interactive Editing of Level Set Models

“…(See discussions related to Equations 19 and 21). For the temporal and spatial discretizations, we use Equations (36) and (37) with advection (hyperbolic speed functions) whereas Equations (34c) and (39) are used for diffusion (e.g. parabolic mean curvature flow).…”

“…Level set methods have been successfully applied in computer graphics, computer vision and visualization [30][31][32][33], for example medical image segmentation [34][35][36], shape morphing [37,38], 3D reconstruction [8,39,40], volume sculpting [41], and the animation of liquids [42].…”

Smooth Surface Extraction from Unstructured Point-based Volume Data Using PDEs

“…Museth et al [20] use a level-set method to segment non-uniform data sets, and Enright et al [5] apply a level-set approach to an octree-based adaptive mesh. Beside these, many other approaches, e.g.…”

Segmentation of thin structures in volumetric medical images

“…As before, it evolves a contour in the image plane, or a surface in volumetric data in order to detect objects with relatively similar intensity levels in the image. A related approach is [45] where a 3D directional edge term is coupled with a smoothing term in order to segment a single object from multiple non-uniform volume data sets.…”