4,121 research outputs found
Segmentation of the left ventricle of the heart in 3-D+t MRI data using an optimized nonrigid temporal model
Modern medical imaging modalities provide large amounts of information in both the spatial and temporal domains and the incorporation of this information in a coherent algorithmic framework is a significant challenge. In this paper, we present a novel and intuitive approach to combine 3-D spatial and temporal (3-D + time) magnetic resonance imaging (MRI) data in an integrated segmentation algorithm to extract the myocardium of the left ventricle. A novel level-set segmentation process is developed that simultaneously delineates and tracks the boundaries of the left ventricle muscle. By encoding prior knowledge about cardiac temporal evolution in a parametric framework, an expectation-maximization algorithm optimally tracks the myocardial deformation over the cardiac cycle. The expectation step deforms the level-set function while the maximization step updates the prior temporal model parameters to perform the segmentation in a nonrigid sense
Dense 3D Face Correspondence
We present an algorithm that automatically establishes dense correspondences
between a large number of 3D faces. Starting from automatically detected sparse
correspondences on the outer boundary of 3D faces, the algorithm triangulates
existing correspondences and expands them iteratively by matching points of
distinctive surface curvature along the triangle edges. After exhausting
keypoint matches, further correspondences are established by generating evenly
distributed points within triangles by evolving level set geodesic curves from
the centroids of large triangles. A deformable model (K3DM) is constructed from
the dense corresponded faces and an algorithm is proposed for morphing the K3DM
to fit unseen faces. This algorithm iterates between rigid alignment of an
unseen face followed by regularized morphing of the deformable model. We have
extensively evaluated the proposed algorithms on synthetic data and real 3D
faces from the FRGCv2, Bosphorus, BU3DFE and UND Ear databases using
quantitative and qualitative benchmarks. Our algorithm achieved dense
correspondences with a mean localisation error of 1.28mm on synthetic faces and
detected anthropometric landmarks on unseen real faces from the FRGCv2
database with 3mm precision. Furthermore, our deformable model fitting
algorithm achieved 98.5% face recognition accuracy on the FRGCv2 and 98.6% on
Bosphorus database. Our dense model is also able to generalize to unseen
datasets.Comment: 24 Pages, 12 Figures, 6 Tables and 3 Algorithm
Cube-Cut: Vertebral Body Segmentation in MRI-Data through Cubic-Shaped Divergences
In this article, we present a graph-based method using a cubic template for
volumetric segmentation of vertebrae in magnetic resonance imaging (MRI)
acquisitions. The user can define the degree of deviation from a regular cube
via a smoothness value Delta. The Cube-Cut algorithm generates a directed graph
with two terminal nodes (s-t-network), where the nodes of the graph correspond
to a cubic-shaped subset of the image's voxels. The weightings of the graph's
terminal edges, which connect every node with a virtual source s or a virtual
sink t, represent the affinity of a voxel to the vertebra (source) and to the
background (sink). Furthermore, a set of infinite weighted and non-terminal
edges implements the smoothness term. After graph construction, a minimal
s-t-cut is calculated within polynomial computation time, which splits the
nodes into two disjoint units. Subsequently, the segmentation result is
determined out of the source-set. A quantitative evaluation of a C++
implementation of the algorithm resulted in an average Dice Similarity
Coefficient (DSC) of 81.33% and a running time of less than a minute.Comment: 23 figures, 2 tables, 43 references, PLoS ONE 9(4): e9338
Hierarchical Object Parsing from Structured Noisy Point Clouds
Object parsing and segmentation from point clouds are challenging tasks
because the relevant data is available only as thin structures along object
boundaries or other features, and is corrupted by large amounts of noise. To
handle this kind of data, flexible shape models are desired that can accurately
follow the object boundaries. Popular models such as Active Shape and Active
Appearance models lack the necessary flexibility for this task, while recent
approaches such as the Recursive Compositional Models make model
simplifications in order to obtain computational guarantees. This paper
investigates a hierarchical Bayesian model of shape and appearance in a
generative setting. The input data is explained by an object parsing layer,
which is a deformation of a hidden PCA shape model with Gaussian prior. The
paper also introduces a novel efficient inference algorithm that uses informed
data-driven proposals to initialize local searches for the hidden variables.
Applied to the problem of object parsing from structured point clouds such as
edge detection images, the proposed approach obtains state of the art parsing
errors on two standard datasets without using any intensity information.Comment: 13 pages, 16 figure
Spotlight the Negatives: A Generalized Discriminative Latent Model
Discriminative latent variable models (LVM) are frequently applied to various
visual recognition tasks. In these systems the latent (hidden) variables
provide a formalism for modeling structured variation of visual features.
Conventionally, latent variables are de- fined on the variation of the
foreground (positive) class. In this work we augment LVMs to include negative
latent variables corresponding to the background class. We formalize the
scoring function of such a generalized LVM (GLVM). Then we discuss a framework
for learning a model based on the GLVM scoring function. We theoretically
showcase how some of the current visual recognition methods can benefit from
this generalization. Finally, we experiment on a generalized form of Deformable
Part Models with negative latent variables and show significant improvements on
two different detection tasks.Comment: Published in proceedings of BMVC 201
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