18,615 research outputs found
Multi-object segmentation using coupled nonparametric shape and relative pose priors
We present a new method for multi-object segmentation in a maximum a posteriori estimation framework. Our method is motivated by the observation that neighboring or coupling objects in images generate configurations and co-dependencies which could potentially aid in segmentation if properly exploited. Our approach employs coupled shape and inter-shape pose priors that are computed using training images in a nonparametric multi-variate kernel density estimation framework. The coupled shape prior is obtained by estimating the joint shape distribution of multiple objects and the inter-shape pose priors are modeled via standard moments. Based on such statistical models, we formulate an optimization problem for segmentation, which we solve by an algorithm based on active contours. Our technique provides significant improvements in the segmentation of weakly contrasted objects in a number of applications. In particular for medical image analysis, we use our method to extract brain Basal Ganglia structures, which are members of a complex multi-object system posing a challenging segmentation problem. We also apply our technique to the problem of handwritten character segmentation. Finally, we use our method to segment cars in urban scenes
Unsupervised Texture Segmentation using Active Contours and Local Distributions of Gaussian Markov Random Field Parameters
In this paper, local distributions of low order Gaussian Markov Random Field (GMRF) model parameters are proposed as texture features for unsupervised texture segmentation.Instead of using model parameters as texture features, we exploit the variations in parameter estimates found by model fitting in local region around the given pixel. Thespatially localized estimation process is carried out by maximum likelihood method employing a moderately small estimation window which leads to modeling of partial texturecharacteristics belonging to the local region. Hence significant fluctuations occur in the estimates which can be related to texture pattern complexity. The variations occurred in estimates are quantified by normalized local histograms. Selection of an accurate window size for histogram calculation is crucial and is achieved by a technique based on the entropy of textures. These texture features expand the possibility of using relativelylow order GMRF model parameters for segmenting fine to very large texture patterns and offer lower computational cost. Small estimation windows result in better boundarylocalization. Unsupervised segmentation is performed by integrated active contours, combining the region and boundary information. Experimental results on statistical and structural component textures show improved discriminative ability of the features compared to some recent algorithms in the literature
Analysis of Amoeba Active Contours
Subject of this paper is the theoretical analysis of structure-adaptive
median filter algorithms that approximate curvature-based PDEs for image
filtering and segmentation. These so-called morphological amoeba filters are
based on a concept introduced by Lerallut et al. They achieve similar results
as the well-known geodesic active contour and self-snakes PDEs. In the present
work, the PDE approximated by amoeba active contours is derived for a general
geometric situation and general amoeba metric. This PDE is structurally similar
but not identical to the geodesic active contour equation. It reproduces the
previous PDE approximation results for amoeba median filters as special cases.
Furthermore, modifications of the basic amoeba active contour algorithm are
analysed that are related to the morphological force terms frequently used with
geodesic active contours. Experiments demonstrate the basic behaviour of amoeba
active contours and its similarity to geodesic active contours.Comment: Revised version with several improvements for clarity, slightly
extended experiments and discussion. Accepted for publication in Journal of
Mathematical Imaging and Visio
FastVentricle: Cardiac Segmentation with ENet
Cardiac Magnetic Resonance (CMR) imaging is commonly used to assess cardiac
structure and function. One disadvantage of CMR is that post-processing of
exams is tedious. Without automation, precise assessment of cardiac function
via CMR typically requires an annotator to spend tens of minutes per case
manually contouring ventricular structures. Automatic contouring can lower the
required time per patient by generating contour suggestions that can be lightly
modified by the annotator. Fully convolutional networks (FCNs), a variant of
convolutional neural networks, have been used to rapidly advance the
state-of-the-art in automated segmentation, which makes FCNs a natural choice
for ventricular segmentation. However, FCNs are limited by their computational
cost, which increases the monetary cost and degrades the user experience of
production systems. To combat this shortcoming, we have developed the
FastVentricle architecture, an FCN architecture for ventricular segmentation
based on the recently developed ENet architecture. FastVentricle is 4x faster
and runs with 6x less memory than the previous state-of-the-art ventricular
segmentation architecture while still maintaining excellent clinical accuracy.Comment: 11 pages, 6 figures, Accepted to Functional Imaging and Modeling of
the Heart (FIMH) 201
Coupled non-parametric shape and moment-based inter-shape pose priors for multiple basal ganglia structure segmentation
This paper presents a new active contour-based, statistical method for simultaneous volumetric segmentation of multiple subcortical structures in the brain. In biological tissues, such as the human brain, neighboring structures exhibit co-dependencies which can aid in segmentation, if properly analyzed and modeled. Motivated by this observation, we formulate the segmentation problem as a maximum a posteriori estimation problem, in which we incorporate statistical prior models on the shapes and inter-shape (relative) poses of the structures of interest. This provides a principled mechanism to bring high level information about the shapes and the relationships of anatomical structures into the segmentation problem. For learning the prior densities we use a nonparametric multivariate kernel density estimation framework. We combine these priors with data in a variational framework and develop an active contour-based iterative segmentation algorithm.
We test our method on the problem of volumetric segmentation of basal ganglia structures in magnetic resonance (MR) images.
We present a set of 2D and 3D experiments as well as a quantitative performance analysis. In addition, we perform a comparison to several existent segmentation methods and demonstrate the improvements provided by our approach in terms of segmentation accuracy
Gaussian mixture model based probabilistic modeling of images for medical image segmentation
In this paper, we propose a novel image segmentation algorithm that is based on the probability distributions of the object and background. It uses the variational level sets formulation with a novel region based term in addition to the edge-based term giving a complementary functional, that can potentially result in a robust segmentation of the images. The main theme of the method is that in most of the medical imaging scenarios, the objects are characterized by some typical characteristics such a color, texture, etc. Consequently, an image can be modeled as a Gaussian mixture of distributions corresponding to the object and background. During the procedure of curve evolution, a novel term is incorporated in the segmentation framework which is based on the maximization of the distance between the GMM corresponding to the object and background. The maximization of this distance using differential calculus potentially leads to the desired segmentation results. The proposed method has been used for segmenting images from three distinct imaging modalities i.e. magnetic resonance imaging (MRI), dermoscopy and chromoendoscopy. Experiments show the effectiveness of the proposed method giving better qualitative and quantitative results when compared with the current state-of-the-art. INDEX TERMS Gaussian Mixture Model, Level Sets, Active Contours, Biomedical Engineerin
Volumetric segmentation of multiple basal ganglia structures
We present a new active contour-based, statistical method for simultaneous volumetric segmentation of multiple subcortical structures in the brain. Neighboring anatomical structures in the human brain exhibit co-dependencies which can aid in segmentation, if properly analyzed and modeled. Motivated by this observation, we formulate the segmentation problem as a maximum a posteriori estimation problem, in which we incorporate statistical prior models
on the shapes and inter-shape (relative) poses of the structures of interest. This provides a principled mechanism to bring high level information about the shapes and the relationships of anatomical structures into the segmentation problem. For learning the prior densities based on training data, we use a nonparametric multivariate kernel density estimation framework.
We combine these priors with data in a variational framework, and develop an active contour-based iterative segmentation algorithm. We test our method on the problem of volumetric segmentation of basal ganglia structures in magnetic resonance (MR) images. We compare our technique with existing methods and demonstrate the improvements it provides in terms of segmentation accuracy
- …