Level Set Segmentation of Biological Volume Datasets

Abstract

This chapter describes level set techniques for extracting surface models from a broad variety of biological volume datasets. These techniques have been incorporated into a more general framework that includes other volume processing algorithms. The volume datasets are produced from standard 3D imaging devices, and are all noisy samplings of complex biological structures with boundaries that have low and often varying contrasts. The level set segmentation method, which is well documented in the literature, creates a new volume from the input data by solving an initial value partial differential equation (PDE) with user-defined feature-extracting terms. Given the local/global nature of these terms, proper initialization of the level set algorithm is extremely important. Thus, level set deformations alone are not sufficient, they must be combined with powerful pre-processing and data analysis techniques in order to produce successful segmentations. This chapter describes the pre-processing and data analysis techniques that have been developed for a number of segmentation applications, as well as the general structure of our framework. Several standard volume processing algorithms have been incorporated into the framework in order to segment datasets generated from MRI, CT and TEM scans.A technique based on moving least-squares has been developed for segmenting multiple non-uniform scans of a single object. New scalar measures have been defined for extracting structures from diffusion tensor MRI scans. Finally, a direct approach to the segmentation of incomplete tomographic data using density parameter estimation is described. These techniques, combined with level set surface deformations, allow us to segment many different types of biological volume datasets.

Microimaging of hairless rat skin by magnetic resonance at 900 MHz

“…The contrast enhancement between lipid-rich sebaceous gland and nonlipid hair body, epidermis and other dermis skin features was achieved by in vivo MRI parametric method to categorize skin structures based on their T2 values [26]. The denoising and surface-fitting segmentation were done by the 'level set' method to create a new volume from the input data to obtain a user-defined feature extraction as described elsewhere [26,27].…”

“…The denoising and surface-fitting segmentation were done by the 'level set' method to create a new volume from the input data to obtain a user-defined feature extraction as described elsewhere [26,27].…”

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).…”

“…where the grid indices (i, j, k) have been omitted for simplicity and S(F) denotes the sign of F. Note that Equation (36) assumes F|∇φ| to be a convex function.…”

“…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].…”