31,768 research outputs found
Disjunctive normal shape and appearance priors with applications to image segmentation
The use of appearance and shape priors in image segmentation is known to improve accuracy; however, existing techniques have several drawbacks. Active shape and appearance models require landmark points and assume unimodal shape and appearance distributions. Level set based shape priors are limited to global shape similarity. In this paper, we present a novel shape and appearance priors for image segmentation based on an implicit parametric shape representation called disjunctive normal shape model (DNSM). DNSM is formed by disjunction of conjunctions of half-spaces defined by discriminants. We learn shape and appearance statistics at varying spatial scales using nonparametric density estimation. Our method can generate a rich set of shape variations by locally combining training shapes. Additionally, by studying the intensity and texture statistics around each discriminant of our shape model, we construct a local appearance probability map. Experiments carried out on both medical and natural image datasets show the potential of the proposed method
A Novel Active Contour Model for Texture Segmentation
Texture is intuitively defined as a repeated arrangement of a basic pattern
or object in an image. There is no mathematical definition of a texture though.
The human visual system is able to identify and segment different textures in a
given image. Automating this task for a computer is far from trivial. There are
three major components of any texture segmentation algorithm: (a) The features
used to represent a texture, (b) the metric induced on this representation
space and (c) the clustering algorithm that runs over these features in order
to segment a given image into different textures. In this paper, we propose an
active contour based novel unsupervised algorithm for texture segmentation. We
use intensity covariance matrices of regions as the defining feature of
textures and find regions that have the most inter-region dissimilar covariance
matrices using active contours. Since covariance matrices are symmetric
positive definite, we use geodesic distance defined on the manifold of
symmetric positive definite matrices PD(n) as a measure of dissimlarity between
such matrices. We demonstrate performance of our algorithm on both artificial
and real texture images
Active Contour Models for Manifold Valued Image Segmentation
Image segmentation is the process of partitioning a image into different
regions or groups based on some characteristics like color, texture, motion or
shape etc. Active contours is a popular variational method for object
segmentation in images, in which the user initializes a contour which evolves
in order to optimize an objective function designed such that the desired
object boundary is the optimal solution. Recently, imaging modalities that
produce Manifold valued images have come up, for example, DT-MRI images, vector
fields. The traditional active contour model does not work on such images. In
this paper, we generalize the active contour model to work on Manifold valued
images. As expected, our algorithm detects regions with similar Manifold values
in the image. Our algorithm also produces expected results on usual gray-scale
images, since these are nothing but trivial examples of Manifold valued images.
As another application of our general active contour model, we perform texture
segmentation on gray-scale images by first creating an appropriate Manifold
valued image. We demonstrate segmentation results for manifold valued images
and texture images
A Q-Ising model application for linear-time image segmentation
A computational method is presented which efficiently segments digital
grayscale images by directly applying the Q-state Ising (or Potts) model. Since
the Potts model was first proposed in 1952, physicists have studied lattice
models to gain deep insights into magnetism and other disordered systems. For
some time, researchers have realized that digital images may be modeled in much
the same way as these physical systems (i.e., as a square lattice of numerical
values). A major drawback in using Potts model methods for image segmentation
is that, with conventional methods, it processes in exponential time. Advances
have been made via certain approximations to reduce the segmentation process to
power-law time. However, in many applications (such as for sonar imagery),
real-time processing requires much greater efficiency. This article contains a
description of an energy minimization technique that applies four Potts
(Q-Ising) models directly to the image and processes in linear time. The result
is analogous to partitioning the system into regions of four classes of
magnetism. This direct Potts segmentation technique is demonstrated on
photographic, medical, and acoustic images.Comment: 7 pages, 8 figures, revtex, uses subfigure.sty. Central European
Journal of Physics, in press (2010
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
Automated detection of brain abnormalities in neonatal hypoxia ischemic injury from MR images.
We compared the efficacy of three automated brain injury detection methods, namely symmetry-integrated region growing (SIRG), hierarchical region splitting (HRS) and modified watershed segmentation (MWS) in human and animal magnetic resonance imaging (MRI) datasets for the detection of hypoxic ischemic injuries (HIIs). Diffusion weighted imaging (DWI, 1.5T) data from neonatal arterial ischemic stroke (AIS) patients, as well as T2-weighted imaging (T2WI, 11.7T, 4.7T) at seven different time-points (1, 4, 7, 10, 17, 24 and 31 days post HII) in rat-pup model of hypoxic ischemic injury were used to assess the temporal efficacy of our computational approaches. Sensitivity, specificity, and similarity were used as performance metrics based on manual ('gold standard') injury detection to quantify comparisons. When compared to the manual gold standard, automated injury location results from SIRG performed the best in 62% of the data, while 29% for HRS and 9% for MWS. Injury severity detection revealed that SIRG performed the best in 67% cases while 33% for HRS. Prior information is required by HRS and MWS, but not by SIRG. However, SIRG is sensitive to parameter-tuning, while HRS and MWS are not. Among these methods, SIRG performs the best in detecting lesion volumes; HRS is the most robust, while MWS lags behind in both respects
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