2,130 research outputs found
Robust Segmentation of Voxel Shapes using Medial Surfaces
We present a new patch-type segmentation method for 3D voxel shapes based on the medial surface, also called surface skeleton. The boundaries of the simplified fore- and background skeletons map one-to-one to increasingly fuzzy, soft convex, respectively concave, edges of the shape. Using this property, we build a method for segmentation of 3D shapes which has several desirable properties. Our method robustly segments both noisy shapes and shapes with soft edges which vanish over low-curvature regions. As the segmentation is based on the skeleton, it reflects the symmetry of the input shape. Finally, multiscale segmentations can be obtained by varying the simplification level of the skeleton. We present a voxel-based implementation of our approach and demonstrate it on several examples.
Patch-type Segmentation of Voxel Shapes using Simplified Surface Skeletons
We present a new method for decomposing a 3D voxel shape into disjoint segments using the shape’s simplified surface-skeleton. The surface skeleton of a shape consists of 2D manifolds inside its volume. Each skeleton point has a maximally inscribed ball that touches the boundary in at least two contact points. A key observation is that the boundaries of the simplified fore- and background skeletons map one-to-one to increasingly fuzzy, soft convex, respectively concave, edges of the shape. Using this property, we build a method for segmentation of 3D shapes which has several desirable properties. Our method segments both noisy shapes and shapes with soft edges which vanish over low-curvature regions. Multiscale segmentations can be obtained by varying the simplification level of the skeleton. We present a voxel-based implementation of our approach and illustrate it on several realistic examples.
3D Geometric Analysis of Tubular Objects based on Surface Normal Accumulation
This paper proposes a simple and efficient method for the reconstruction and
extraction of geometric parameters from 3D tubular objects. Our method
constructs an image that accumulates surface normal information, then peaks
within this image are located by tracking. Finally, the positions of these are
optimized to lie precisely on the tubular shape centerline. This method is very
versatile, and is able to process various input data types like full or partial
mesh acquired from 3D laser scans, 3D height map or discrete volumetric images.
The proposed algorithm is simple to implement, contains few parameters and can
be computed in linear time with respect to the number of surface faces. Since
the extracted tube centerline is accurate, we are able to decompose the tube
into rectilinear parts and torus-like parts. This is done with a new linear
time 3D torus detection algorithm, which follows the same principle of a
previous work on 2D arc circle recognition. Detailed experiments show the
versatility, accuracy and robustness of our new method.Comment: in 18th International Conference on Image Analysis and Processing,
Sep 2015, Genova, Italy. 201
Skeletonization and segmentation of binary voxel shapes
Preface. This dissertation is the result of research that I conducted between January 2005 and December 2008 in the Visualization research group of the Technische Universiteit Eindhoven. I am pleased to have the opportunity to thank a number of people that made this work possible. I owe my sincere gratitude to Alexandru Telea, my supervisor and first promotor. I did not consider pursuing a PhD until my Master’s project, which he also supervised. Due to our pleasant collaboration from which I learned quite a lot, I became convinced that becoming a doctoral student would be the right thing to do for me. Indeed, I can say it has greatly increased my knowledge and professional skills. Alex, thank you for our interesting discussions and the freedom you gave me in conducting my research. You made these four years a pleasant experience. I am further grateful to Jack vanWijk, my second promotor. Our monthly discussions were insightful, and he continuously encouraged me to take a more formal and scientific stance. I would also like to thank Prof. Jan de Graaf from the department of mathematics for our discussions on some of my conjectures. His mathematical rigor was inspiring. I am greatly indebted to the Netherlands Organisation for Scientific Research (NWO) for funding my PhD project (grant number 612.065.414). I thank Prof. Kaleem Siddiqi, Prof. Mark de Berg, and Dr. Remco Veltkamp for taking part in the core doctoral committee and Prof. Deborah Silver and Prof. Jos Roerdink for participating in the extended committee. Our Visualization group provides a great atmosphere to do research in. In particular, I would like to thank my fellow doctoral students Frank van Ham, Hannes Pretorius, Lucian Voinea, Danny Holten, Koray Duhbaci, Yedendra Shrinivasan, Jing Li, NielsWillems, and Romain Bourqui. They enabled me to take my mind of research from time to time, by discussing political and economical affairs, and more trivial topics. Furthermore, I would like to thank the senior researchers of our group, Huub van de Wetering, Kees Huizing, and Michel Westenberg. In particular, I thank Andrei Jalba for our fruitful collaboration in the last part of my work. On a personal level, I would like to thank my parents and sister for their love and support over the years, my friends for providing distractions outside of the office, and Michelle for her unconditional love and ability to light up my mood when needed
Segmentation of 3D pore space from CT images using curvilinear skeleton: application to numerical simulation of microbial decomposition
Recent advances in 3D X-ray Computed Tomographic (CT) sensors have stimulated
research efforts to unveil the extremely complex micro-scale processes that
control the activity of soil microorganisms. Voxel-based description (up to
hundreds millions voxels) of the pore space can be extracted, from grey level
3D CT scanner images, by means of simple image processing tools. Classical
methods for numerical simulation of biological dynamics using mesh of voxels,
such as Lattice Boltzmann Model (LBM), are too much time consuming. Thus, the
use of more compact and reliable geometrical representations of pore space can
drastically decrease the computational cost of the simulations. Several recent
works propose basic analytic volume primitives (e.g. spheres, generalized
cylinders, ellipsoids) to define a piece-wise approximation of pore space for
numerical simulation of draining, diffusion and microbial decomposition. Such
approaches work well but the drawback is that it generates approximation
errors. In the present work, we study another alternative where pore space is
described by means of geometrically relevant connected subsets of voxels
(regions) computed from the curvilinear skeleton. Indeed, many works use the
curvilinear skeleton (3D medial axis) for analyzing and partitioning 3D shapes
within various domains (medicine, material sciences, petroleum engineering,
etc.) but only a few ones in soil sciences. Within the context of soil
sciences, most studies dealing with 3D medial axis focus on the determination
of pore throats. Here, we segment pore space using curvilinear skeleton in
order to achieve numerical simulation of microbial decomposition (including
diffusion processes). We validate simulation outputs by comparison with other
methods using different pore space geometrical representations (balls, voxels).Comment: preprint, submitted to Computers & Geosciences 202
Shape Priors in Medical Image Analysis: Extensions of the Level Set Method
The 3D medical image segmentation problem typically involves assigning labels to 3D pixels, called voxels, which comprise a given medical volume. In its simplest form the segmentation problem involves assigning the labels part of the structure of interest or not part of the structure to each voxel using locally measured properties and prior knowledge of human anatomy. Robust segmentation remains an open research problem today due to the significant challenges in the task including: partial volume averaging, overlapping intensity distributions and image noise. In the face of these challenges prior knowledge needs to be added to make the segmentation methods more robust. Active contours were introduced in the late 1980\u27s mainly to address situations in which the object to be segmented had a single closed boundary. To address situations in which the object(s) to be segmented have unknown topology the level set framework was recently introduced to segment medical images. Unlike active contours, the level set method relies on an implicit shape representation rather than an explicit shape representation and hence new methods to impose prior knowledge about expected shape have to be devised for the new framework. This paper explores recent segmentation methods from four research groups which address the task of imposing prior knowledge of shape for object boundary segmentation. Three of the methods impose priors onto the level set technique and one employs a medial axis shape representation and Statistical shape information to guide a model-based segmentation. All of the methods include a notion of a statistical shape distribution. Each method is described, analyzed for its strengths and weaknesses. The paper concludes with a comparison of all four methods and recommendations for their applicability
Multi-Surface Simplex Spine Segmentation for Spine Surgery Simulation and Planning
This research proposes to develop a knowledge-based multi-surface simplex deformable model for segmentation of healthy as well as pathological lumbar spine data. It aims to provide a more accurate and robust segmentation scheme for identification of intervertebral disc pathologies to assist with spine surgery planning. A robust technique that combines multi-surface and shape statistics-aware variants of the deformable simplex model is presented. Statistical shape variation within the dataset has been captured by application of principal component analysis and incorporated during the segmentation process to refine results. In the case where shape statistics hinder detection of the pathological region, user-assistance is allowed to disable the prior shape influence during deformation. Results have been validated against user-assisted expert segmentation
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