4,789 research outputs found
Image Segmentation Using Weak Shape Priors
The problem of image segmentation is known to become particularly challenging
in the case of partial occlusion of the object(s) of interest, background
clutter, and the presence of strong noise. To overcome this problem, the
present paper introduces a novel approach segmentation through the use of
"weak" shape priors. Specifically, in the proposed method, an segmenting active
contour is constrained to converge to a configuration at which its geometric
parameters attain their empirical probability densities closely matching the
corresponding model densities that are learned based on training samples. It is
shown through numerical experiments that the proposed shape modeling can be
regarded as "weak" in the sense that it minimally influences the segmentation,
which is allowed to be dominated by data-related forces. On the other hand, the
priors provide sufficient constraints to regularize the convergence of
segmentation, while requiring substantially smaller training sets to yield less
biased results as compared to the case of PCA-based regularization methods. The
main advantages of the proposed technique over some existing alternatives is
demonstrated in a series of experiments.Comment: 27 pages, 8 figure
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
Achieving the Way for Automated Segmentation of Nuclei in Cancer Tissue Images through Morphology-Based Approach: a Quantitative Evaluation
In this paper we address the problem of nuclear segmentation in cancer tissue images, that is critical for specific protein activity quantification and for cancer diagnosis and therapy. We present a fully automated morphology-based technique able to perform accurate nuclear segmentations in images with heterogeneous staining and multiple tissue layers and we compare it with an alternate semi-automated method based on a well established segmentation approach, namely active contours. We discuss active contours’ limitations in the segmentation of immunohistochemical images and we demonstrate and motivate through extensive experiments the better accuracy of our fully automated approach compared to various active contours implementations
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Shape-driven segmentation of the arterial wall in intravascular ultrasound images
Segmentation of arterial wall boundaries from intravascular images is an important problem for many applications in the study of plaque characteristics, mechanical properties of the arterial wall, its 3D reconstruction,
and its measurements such as lumen size, lumen radius, and wall radius. We present a shape-driven approach to segmentation of the arterial wall from intravascular ultrasound images in the rectangular domain. In a properly built
shape space using training data, we constrain the lumen and media-adventitia contours to a smooth, closed geometry, which increases the segmentation quality without any tradeoff with a regularizer term. In addition to a shape prior,
we utilize an intensity prior through a non-parametric probability density based image energy, with global image measurements rather than pointwise measurements used in previous methods. Furthermore, a detection step is included to address the challenges introduced to the segmentation process by side branches and calcifications. All these features greatly enhance our segmentation method. The tests of our algorithm on a large dataset demonstrate the effectiveness of our approach
Segmentation of Three-dimensional Images with Parametric Active Surfaces and Topology Changes
In this paper, we introduce a novel parametric method for segmentation of
three-dimensional images. We consider a piecewise constant version of the
Mumford-Shah and the Chan-Vese functionals and perform a region-based
segmentation of 3D image data. An evolution law is derived from energy
minimization problems which push the surfaces to the boundaries of 3D objects
in the image. We propose a parametric scheme which describes the evolution of
parametric surfaces. An efficient finite element scheme is proposed for a
numerical approximation of the evolution equations. Since standard parametric
methods cannot handle topology changes automatically, an efficient method is
presented to detect, identify and perform changes in the topology of the
surfaces. One main focus of this paper are the algorithmic details to handle
topology changes like splitting and merging of surfaces and change of the genus
of a surface. Different artificial images are studied to demonstrate the
ability to detect the different types of topology changes. Finally, the
parametric method is applied to segmentation of medical 3D images
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