Marching cubes: A high resolution 3D surface construction algorithm

Lorensen, William E.; Cline, H. E.

doi:10.1145/37402.37422

Cited by 8,568 publications

(4,038 citation statements)

References 23 publications

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“…The 3D view is used to render the boundaries of segmented structures that are computed using the contour extraction, decimation and fairing algorithms in the Visualization Toolkit (VTK) (Lorensen and Cline, 1987;Schroeder et al, 1996). The user can reposition the 3D cursor by clicking on one of these sur-…”

Section: Image Navigation and Manual Segmentationmentioning

confidence: 99%

User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability

Yushkevich¹,

Piven²,

Hazlett³

et al. 2006

NeuroImage

7,373

4,931

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Active contour segmentation and its robust implementation using level set methods are well established theoretical approaches that have been studied thoroughly in the image analysis literature. Despite the existence of these powerful segmentation methods, the needs of clinical research continue to be fulfilled, to a large extent, using slice-by-slice manual tracing. To bridge the gap between methodological advances and clinical routine, we developed an open source application called ITK-SNAP, which is intended to make level set segmentation easily accessible to a wide range of users, including those with little or no mathematical expertise. This paper describes the methods and software engineering philosophy behind this new tool and provides the results of validation experiments performed in the context of an ongoing child autism neuroimaging study. The validation establishes SNAP intra/interrater reliability and overlap error statistics for the caudate nucleus and finds that SNAP is a highly reliable and efficient alternative to manual tracing.Analogous results for lateral ventricle segmentation are provided.

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Section: Image Navigation and Manual Segmentationmentioning

confidence: 99%

User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability

Yushkevich¹,

Piven²,

Hazlett³

et al. 2006

NeuroImage

7,373

4,931

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show abstract

“…After the tissue classification, the cortical surface is usually generated as a smooth triangular mesh. The most widely used method for triangulating the surface is the marching cubes algorithm (Lorensen and Cline, 1987). Level set method (Sethian, 1996) or deformable surfaces method (Davatzikos, 1995) are also available.…”

Section: Introductionmentioning

confidence: 99%

Deformation-based surface morphometry applied to gray matter deformation

Chung¹,

Worsley²,

Robbins³

et al. 2003

NeuroImage

255

191

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We present a unified statistical approach to deformation-based morphometry applied to the cortical surface. The cerebral cortex has the topology of a 2D highly convoluted sheet. As the brain develops over time, the cortical surface area, thickness, curvature and total gray matter volume change. It is highly likely that such age-related surface changes are not uniform. By measuring how such surface metrics change over time, the regions of the most rapid structural changes can be localized. We avoided using surface flattening, which distorts the inherent geometry of the cortex in our analysis and it is only used in visualization. To increase the signal to noise ratio, diffusion smoothing, which generalizes Gaussian kernel smoothing to an arbitrary curved cortical surface, has been developed and applied to surface data. Afterwards, statistical inference on the cortical surface will be performed via random fields theory. As an illustration, we demonstrate how this new surface-based morphometry can be applied in localizing the cortical regions of the gray matter tissue growth and loss in the brain images longitudinally collected in the group of children and adolescents.

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“…To shape the 3D volume of the segmented tissues, the marching cubes algorithm is used as described in [21]. The algorithm locates the surface in a virtual cube created from eight pixels, which constitute the eight vertices of the cube (four each from two adjacent cross-sections).…”

Section: D Reconstructionmentioning

confidence: 99%

Jaw tissues segmentation in dental 3D CT images using fuzzy-connectedness and morphological processing

Llorens

Naranjo

López

et al. 2012

Computer Methods and Programs in Biomedicine

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ElsevierLloréns Rodríguez, R.; Naranjo Ornedo, V.; López-Mir, F.; Alcañiz Raya, ML. (2012). Jaw tissues segmentation in dental 3D CT images using fuzzy-connectedness and morphological processing. Computer Methods and Programs in Biomedicine. 108 (2) AbstractThe success of oral surgery is subject to accurate advanced planning. In order to properly plan for dental surgery or a suitable implant placement, it is necessary an accurate segmentation of the jaw tissues: the teeth, the cortical bone, the trabecular core and over all, the inferior alveolar nerve. This manuscript presents a new automatic method that is based on fuzzy connectedness object extraction and mathematical morphology processing. The method uses computed tomography data to extract different views of the jaw: a pseudo-orthopantomographic view to estimate the path of the nerve and cross-sectional views to segment the jaw tissues. The method has been tested in a groundtruth set consisting of more than 9000 cross-sections from 20 different patients and has been evaluated using four similarity indicators (the Jaccard index, Dice's coefficient, point-to-point and point-tocurve distances), achieving promising results in all of them (0.726 ± 0.031, 0.840 ± .019, 0.144 ± 0.023 mm and 0.163 ± 0.025 mm, respectively). The method has proven to be significantly automated and accurate, with errors around 5% (of the diameter of the nerve), and is easily integrable in current dental planning systems.

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Marching cubes: A high resolution 3D surface construction algorithm

Cited by 8,568 publications

References 23 publications

User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability

User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability

Deformation-based surface morphometry applied to gray matter deformation

Jaw tissues segmentation in dental 3D CT images using fuzzy-connectedness and morphological processing

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