Globally Optimal Image Segmentation with an Elastic Shape Prior

Schoenemann, Thomas; Cremers, Daniel

doi:10.1109/iccv.2007.4408972

Cited by 38 publications

(30 citation statements)

References 15 publications

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“…Strzodka et al (2003) instead used a GPU accelerated version of the Hough transform for object recognition and pose detection. Schoenemann and Cremers (2007) took advantage of a GPU for globally optimal segmentation based on shape priors while Kauffmann and Piche (2010) accelerated a graph-cut based segmentation algorithm, which they applied to segmentation of organs in medical datasets. During recent years, many reports on CUDA implementations of a large variety of segmentation algorithms have been published.…”

Section: Image Segmentationmentioning

confidence: 99%

Medical image processing on the GPU – Past, present and future

Eklund

Dufort

Forsberg

et al. 2013

Medical Image Analysis

348

198

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Graphics processing units (GPUs) are used today in a wide range of applications, mainly because they can dramatically accelerate parallel computing, are affordable and energy efficient. In the field of medical imaging, GPUs are in some cases crucial for enabling practical use of computationally demanding algorithms. This review presents the past and present work on GPU accelerated medical image processing, and is meant to serve as an overview and introduction to existing GPU implementations. The review covers GPU acceleration of basic image processing operations (filtering, interpolation, histogram estimation and distance transforms), the most commonly used algorithms in medical imaging (image registration, image segmentation and image denoising) and algorithms that are specific to individual modalities (CT, PET, SPECT, MRI, fMRI, DTI, ultrasound, optical imaging and microscopy). The review ends by highlighting some future possibilities and challenges.

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Section: Image Segmentationmentioning

confidence: 99%

Medical image processing on the GPU – Past, present and future

Eklund

Dufort

Forsberg

et al. 2013

Medical Image Analysis

348

198

View full text Add to dashboard Cite

show abstract

“…The graph methods of Felzenszwalb [11] and Schoenemann and Cremers [26] can segment objects under elastic deformations without needing any initialization and guarantee globally optimal solutions. In [11], nonserial dynamic programming is used to find the optimal matching between a deformable template represented by triangulated polygons and the image pixels.…”

Section: Introductionmentioning

confidence: 99%

“…In [11], nonserial dynamic programming is used to find the optimal matching between a deformable template represented by triangulated polygons and the image pixels. In [26], the segmentation is found by computing the minimal ratio cycle in a product graph of the image and a shape template parameterized by arc length. Both of these methods can be slow in practice, with runtimes of up to several minutes on typical CPUs.…”

Section: Introductionmentioning

confidence: 99%

Shape prior segmentation of multiple objects with graph cuts

Manjunath

2008

2008 IEEE Conference on Computer Vision and Pattern Recognition

134

102

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We present a new shape prior segmentation method using graph cuts capable of segmenting multiple objects. The shape prior energy is based on a shape distance popular with level set approaches. We also present a multiphase graph cut framework to simultaneously segment multiple, possibly overlapping objects. The multiphase formulation differs from multiway cuts in that the former can account for object overlaps by allowing a pixel to have multiple labels. We then extend the shape prior energy to encompass multiple shape priors. Unlike variational methods, a major advantage of our approach is that the segmentation energy is minimized directly without having to compute its gradient, which can be a cumbersome task and often relies on approximations. Experiments demonstrate that our algorithm can cope with image noise and clutter, as well as partial occlusions and affine transformations of the shape.

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“…The main challenge is to determine the corresponding optimal solution that is often challenging with gradientbased approaches [18]. On the other hand, discrete methods [2] could yield a better minimum of the objective function under certain constraints but the integration of global deformable priors [9,16] is not straightforward. The aim of our approach is to address the above-mentioned limitations of conventional knowledge-based segmentation approaches.…”

Section: Introductionmentioning

confidence: 99%

Shape priors and discrete MRFs for knowledge-based segmentation

Besbes

Komodakis

Paragios

2009

2009 IEEE Conference on Computer Vision and Pattern Recognition

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In this paper we introduce a new approach to knowledgebased segmentation. Our method consists of a novel representation to model shape variations as well as an efficient inference procedure to fit the model to new data. The considered shape model is similarity-invariant and refers to an incomplete graph that consists of intra and intercluster connections representing the inter-dependencies of control points. The clusters are determined according to the co-dependencies of the deformations of the control points within the training set. The connections between the components of a cluster represent the local structure while the connections between the clusters account for the global structure. The distributions of the normalized distances between the connected control points encode the prior model. During search, this model is used together with a discrete markov random field (MRF) based segmentation, where the unknown variables are the positions of the control points in the image domain. To encode the image support, a Voronoi decomposition of the domain is considered and regional based statistics are used. The resulting model is computationally efficient, can encode complex statistical models of shape variations and benefits from the image support of the entire spatial domain.

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Globally Optimal Image Segmentation with an Elastic Shape Prior

Cited by 38 publications

References 15 publications

Medical image processing on the GPU – Past, present and future

Medical image processing on the GPU – Past, present and future

Shape prior segmentation of multiple objects with graph cuts

Shape priors and discrete MRFs for knowledge-based segmentation

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