1,303 research outputs found
Characterizing Width Uniformity by Wave Propagation
This work describes a novel image analysis approach to characterize the
uniformity of objects in agglomerates by using the propagation of normal
wavefronts. The problem of width uniformity is discussed and its importance for
the characterization of composite structures normally found in physics and
biology highlighted. The methodology involves identifying each cluster (i.e.
connected component) of interest, which can correspond to objects or voids, and
estimating the respective medial axes by using a recently proposed wavefront
propagation approach, which is briefly reviewed. The distance values along such
axes are identified and their mean and standard deviation values obtained. As
illustrated with respect to synthetic and real objects (in vitro cultures of
neuronal cells), the combined use of these two features provide a powerful
description of the uniformity of the separation between the objects, presenting
potential for several applications in material sciences and biology.Comment: 14 pages, 23 figures, 1 table, 1 referenc
Hieroglyph: Hierarchical Glia Graph Skeletonization and Matching
Automatic 3D reconstruction of glia morphology is a powerful tool necessary
for investigating the role of microglia in neurological disorders in the
central nervous system. Current glia skeleton reconstruction techniques fail to
capture an accurate tracing of the processes over time, useful for the study of
the microglia motility and morphology in the brain during healthy and diseased
states. We propose Hieroglyph, a fully automatic temporal 3D skeleton
reconstruction algorithm for glia imaged via 3D multiphoton microscopy.
Hieroglyph yielded a 21% performance increase compared to state of the art
automatic skeleton reconstruction methods and outperforms the state of the art
in different measures of consistency on datasets of 3D images of microglia. The
results from this method provide a 3D graph and digital reconstruction of glia
useful for a myriad of morphological analyses that could impact studies in
brain immunology and disease.Comment: submitted to IEEE International Conference on Image Processing, 201
Robust Feature Detection and Local Classification for Surfaces Based on Moment Analysis
The stable local classification of discrete surfaces with respect to features such as edges and corners or concave and convex regions, respectively, is as quite difficult as well as indispensable for many surface processing applications. Usually, the feature detection is done via a local curvature analysis. If concerned with large triangular and irregular grids, e.g., generated via a marching cube algorithm, the detectors are tedious to treat and a robust classification is hard to achieve. Here, a local classification method on surfaces is presented which avoids the evaluation of discretized curvature quantities. Moreover, it provides an indicator for smoothness of a given discrete surface and comes together with a built-in multiscale. The proposed classification tool is based on local zero and first moments on the discrete surface. The corresponding integral quantities are stable to compute and they give less noisy results compared to discrete curvature quantities. The stencil width for the integration of the moments turns out to be the scale parameter. Prospective surface processing applications are the segmentation on surfaces, surface comparison, and matching and surface modeling. Here, a method for feature preserving fairing of surfaces is discussed to underline the applicability of the presented approach.
- …