The level set method of surface representation and deformation has found many applications in image processing, especially with regard to segmentation. Naive numerical solutions have long since given way to much more efficient narrow band methods, where updates to the scalar field are performed only within a number of layers either side of the surface. This paper presents our implementation of automated segmentation via the sparse field narrow band approach. We use k-means clustering of regularly sampled points to provide initialisation of seed locations and mean voxel values, removing the need for manual seed and parameter selection. We demonstrate this method by segmenting grey matter and white matter from a simulated T1 MRI scan.Anisotropy can easily cause this and other segmentation methods to misclassify voxels, however. Often it is clear in a 3D view of the resulting surface which regions require refinement. With available extensions to the sparse field and rendering algorithms we can now efficiently perform purely localised modifications and re-rendering of the surface. These extensions, plus properties inherent in the level set method, allow us to easily situate swarms of agents on the surface, intelligently inhabiting and modifying the zero-level set. By a simple weighting of movement direction, we can direct the swarm to any user-specified point on the surface in order to affect repairs and refinement.978-1-4244-8126-2/10/$26.00 ©2010 IEEE

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This paper presents a novel 3D brain segmentation method based on level sets and bio-inspired methodologies. Level set segmentation methods, although highly promising, require manual selection of seed positions and thereshold parameters, along with manual reinitialisation to a new level set surface for each candidate region. Here, the use of swarm intelligent mechanisms is used to provide all the statistical data and sample points required, allowing automatic initialisation of multiple level set solvers. This is shown by segmentation of white matter, grey matter and cerebro-spinal fluid in a simulated T1 MRI scan, followed by direct comparison between a commercial level application-FMRIB's FAST-and the ground truth anatomical model.