Computing geodesics and minimal surfaces via graph cuts

Boykov,; Kolmogorov,

doi:10.1109/iccv.2003.1238310

Cited by 460 publications

(532 citation statements)

References 26 publications

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“…To validate our segmentation method as it pertains to novel data, we perform leave-one-out validation on the set of 470 images using energy functional (5). Under error metric ε, we found the average error to be 0.13, improving over the average error of 0.16 reported in [9].…”

Section: Methodsmentioning

confidence: 99%

“…(2)), as originally presented in [9], we performed validation on the set of 470 images using energy functional (5). For each image, the optimal parameters are learned directly, using the ground truth segmentation, rather than interpolation from the manifold.…”

Section: Methodsmentioning

confidence: 99%

“…A gradient descent-based solver is typically used but combinatorial approaches have also been explored for discretized versions of the problem [5]. Here we focus on continuous problems, and thus assume a gradient descent solver.…”

Section: Theory: Notations and Uncertainty In Segmentationmentioning

confidence: 99%

“…Papers dealing with this issue are now common ground for both continuous [7,8] and discrete [5,6] optimization, but they all have uncertainty stemming from the free weights in their energy functionals.…”

Section: Introductionmentioning

confidence: 99%

“…For this and other reasons, the automatic segmentation of medical images remains a daunting task. Many segmentation approaches rely on the minimization of objective functions, including several landmark papers: from the seminal paper of Snakes for 2D segmentation [1] and other explicit models [2] to implicit models [3,4], graph approaches [5,6], and variants thereof.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Optimal Weights for Convex Functionals in Medical Image Segmentation

McIntosh

Hamarneh

2009

Advances in Visual Computing

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Abstract.Energy functional minimization is a popular technique for medical image segmentation. The segmentation must be initialized, weights for competing terms of an energy functional must be tuned, and the functional minimized. There is a substantial amount of guesswork involved. We reduce this guesswork by analytically determining the optimal weights and minimizing a convex energy functional independent of the initialization. We demonstrate improved results over state of the art on a set of 470 clinical examples.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Theory: Notations and Uncertainty In Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal Weights for Convex Functionals in Medical Image Segmentation

McIntosh

Hamarneh

2009

Advances in Visual Computing

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Evolutionary Deformable Models for Medical Image Segmentation: A Genetic Algorithm Approach to Optimizing Learned, Intuitive, and Localized Medial‐Based Shape Deformation

McIntosh¹,

Hamarneh²

2010

Genetic and Evolutionary Computation

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We present a novel evolutionary computing based approach to medical image segmentation. Our method complements the image-pixel integration power of deformable shape models with the high-level control mechanisms of genetic algorithms (GA). Specifically, GA alleviate typical deformable model weaknesses pertaining to model initialization, deformation parameter selection, and energy functional local minima through the simultaneous evolution of a large number of models. Furthermore, we constrain the evolution, and thus reduce the size of the search-space, by using statistically-based deformable models whose deformations are intuitive (stretch, bulge, bend) and driven in-terms of principal modes of variation of a learned mean shape. We demonstrate our work through its application to corpus callosum segmentation in mid-sagittal brain magnetic resonance images (MRI).

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Detail‐feature‐preserving surface reconstruction

Zhou

Duan

et al. 2012

Computer Animation & Virtual

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In this paper, we propose a feature-preserving surface reconstruction method from sparse noisy 3D measurements such as range scanning or passive multiview stereo. In contrast to earlier methods, we define a novel type of explicit 3D filter-regularized weighted least squares filter-to characterize the detail features such as surface wrinkles and sharp features. To account for noise, we rasterize input-oriented points into a probabilistic volume (base volume) and then create a guidance volume by Gaussian filtering. Both the base volume and the guidance volume are further filtered by regularized weighted least squares filter to detect and recover detail features. After the two-stage filtering, a global minimal surface is computed by graph cut and meshed as a geometric model. Experimental results on various datasets show that our method is robust to noise, outliers, and missing parts, which makes it more suitable to fit indoor/outdoor multiview stereo data. Unlike other methods, our method can completely recover scene structures and preserve detail features from noisy point samples.

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Computing geodesics and minimal surfaces via graph cuts

Abstract: Abstract

Cited by 460 publications

References 26 publications

Optimal Weights for Convex Functionals in Medical Image Segmentation

Optimal Weights for Convex Functionals in Medical Image Segmentation

Evolutionary Deformable Models for Medical Image Segmentation: A Genetic Algorithm Approach to Optimizing Learned, Intuitive, and Localized Medial‐Based Shape Deformation

Detail‐feature‐preserving surface reconstruction

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