Shape prior segmentation of multiple objects with graph cuts

Vu, Nhat; Manjunath, B.S.

doi:10.1109/cvpr.2008.4587450

Cited by 134 publications

(109 citation statements)

References 33 publications

(45 reference statements)

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“…They then employ many iterations where a preinitialized binary mask is updated to get the final segmentation. Vu et al [45] use a discrete version of shape distance functions to segment multiple objects, which can be cumbersome. A flux-maximization approach was used in Chittajallu et al [46] to include prior shape information, while in Veksler [47], the smoothness cost was modified to include star shape priors.…”

Section: Introductionmentioning

confidence: 99%

Cardiac Image Segmentation from Cine Cardiac MRI Using Graph Cuts and Shape Priors

Mahapatra

2013

J Digit Imaging

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In this paper, we propose a novel method for segmentation of the left ventricle, right ventricle, and myocardium from cine cardiac magnetic resonance images of the STACOM database. Our method incorporates prior shape information in a graph cut framework to achieve segmentation. Poor edge information and large within-patient shape variation of the different parts necessitates the inclusion of prior shape information. But large interpatient shape variability makes it difficult to have a generalized shape model. Therefore, for every dataset the shape prior is chosen as a single image clearly showing the different parts. Prior shape information is obtained from a combination of distance functions and orientation angle histograms of each pixel relative to the prior shape. To account for shape changes, pixels near the boundary are allowed to change their labels by appropriate formulation of the penalty and smoothness costs. Our method consists of two stages. In the first stage, segmentation is performed using only intensity information which is the starting point for the second stage combining intensity and shape information to get the final segmentation. Experimental results on different subsets of 30 real patient datasets show higher segmentation accuracy in using shape information and our method's superior performance over other competing methods.

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Section: Introductionmentioning

confidence: 99%

Cardiac Image Segmentation from Cine Cardiac MRI Using Graph Cuts and Shape Priors

Mahapatra

2013

J Digit Imaging

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“…Though intensive research has been done, accurate segmentation of 3-D soft tissues is still a challenging problem. The main difficulties lie in the following aspects [1,2]: First, soft tissues often present a large variation in both shape and size. Second, the target objects often lack strong boundaries and have similar intensity information.…”

Section: Introductionmentioning

confidence: 99%

Graph Search with Appearance and Shape Information for 3-D Prostate and Bladder Segmentation

Song

Liu

et al. 2010

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010

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Abstract. The segmentation of soft tissues in medical images is a challenging problem due to the weak boundary, large deformation and serious mutual influence. We present a novel method incorporating both the shape and appearance information in a 3-D graph-theoretic framework to overcome those difficulties for simultaneous segmentation of prostate and bladder. An arc-weighted graph is constructed corresponding to the initial mesh. Both the boundary and region information is incorporated into the graph with learned intensity distribution, which drives the mesh to the best fit of the image. A shape prior penalty is introduced by adding weighted-arcs in the graph, which maintains the original topology of the model and constraints the flexibility of the mesh. The surface-distance constraints are enforced to avoid the leakage between prostate and bladder. The target surfaces are found by solving a maximum flow problem in low-order polynomial time. Both qualitative and quantitative results on prostate and bladder segmentation were promising, proving the power of our algorithm.

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“…Since color and gradient information is however not always sufficient for a plausible segmentation, several attempts were made to integrate shape priors into the optimization process [4,7,9,14]. For the special case of faces, the authors of [10] propose to use an elliptical shape prior.…”

Section: Related Workmentioning

confidence: 99%

System for the automated segmentation of heads from arbitrary background

Prestele

Schneider

Eisert

2011

2011 18th IEEE International Conference on Image Processing

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Abstract. We propose a system for the fully automated segmentation of frontal human head portraits from arbitrary unknown background. No user interaction is required at all, since the system is initialized using a standard eye detector. Using this semantic information, the head region is projected into a normalized polar reference frame. To setup an energy function for segmentation, regional and boundary models are learned from the image data. A robust non-local boundary detection scheme is proposed, which minimizes the similarity of fore-and background regions. Additionally a shape model learned from a large set of manually segmented images is employed as prior information to encourage the segmentation of plausible head shapes. Segmentation is performed as an iterative optimization process, using two different graph-based algorithms.

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Shape prior segmentation of multiple objects with graph cuts

Cited by 134 publications

References 33 publications

Cardiac Image Segmentation from Cine Cardiac MRI Using Graph Cuts and Shape Priors

Cardiac Image Segmentation from Cine Cardiac MRI Using Graph Cuts and Shape Priors

Graph Search with Appearance and Shape Information for 3-D Prostate and Bladder Segmentation

System for the automated segmentation of heads from arbitrary background

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