Probabilistic segmentation of partial volume voxels

Vincken, Koen L.; Koster, André S. E.; Viergever, Max A.

doi:10.1016/0167-8655(94)90139-2

Cited by 24 publications

(11 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A number of methods have been suggested to overcome this limitation, and to address the issue of partial volume effect corrections. Common methods are based on expectationmaximization [24], scale-space approaches [25], wavelets [26], Markov random fields [27], fuzzy techniques [28], etc.…”

Section: Pixel Coverage Segmentationmentioning

confidence: 99%

A graph-based framework for sub-pixel image segmentation

Malmberg

Lindblad

Sladoje

et al. 2011

Theoretical Computer Science

View full text Add to dashboard Cite

a b s t r a c tMany image segmentation methods utilize graph structures for representing images, where the flexibility and generality of the abstract structure is beneficial. By using a fuzzy object representation, i.e., allowing partial belongingness of elements to image objects, the unavoidable loss of information when representing continuous structures by finite sets is significantly reduced, enabling feature estimates with sub-pixel precision.This work presents a framework for object representation based on fuzzy segmented graphs. Interpreting the edges as one-dimensional paths between the vertices of a graph, we extend the notion of a graph cut to that of a located cut, i.e., a cut with sub-edge precision. We describe a method for computing a located cut from a fuzzy segmentation of graph vertices. Further, the notion of vertex coverage segmentation is proposed as a graph theoretic equivalent to pixel coverage segmentations and a method for computing such a segmentation from a located cut is given. Utilizing the proposed framework, we demonstrate improved precision of area measurements of synthetic two-dimensional objects. We emphasize that although the experiments presented here are performed on two-dimensional images, the proposed framework is defined for general graphs and thus applicable to images of any dimension.

show abstract

Section: Pixel Coverage Segmentationmentioning

confidence: 99%

A graph-based framework for sub-pixel image segmentation

Malmberg

Lindblad

Sladoje

et al. 2011

Theoretical Computer Science

View full text Add to dashboard Cite

show abstract

“…The identified voxels within each gray value range were further processed in order to address the partial voxel effect [28]. At the edge of glass ballotini particles, voxels are comprised of both glass ballotini and air.…”

Section: Image Segmentationmentioning

confidence: 99%

Measuring granule phase volume distributions using X-ray microtomography

2014

View full text Add to dashboard Cite

“…The hyperstack (for a more detailed description and extensions see [28,15,161) is a linking model based segmentation technique. The entire process requires four steps (see In the blurring phase a stack of images is created.…”

Section: The Hyperstackmentioning

confidence: 99%

Comparison of multiscale representations for a linking-based image segmentation model

Niessen¹,

Vincken²,

Viergever³

1996

Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis

View full text Add to dashboard Cite

Different multiscale generators are qualitatively compared with respect to theirper$ormance within a multiscale linking model f o r image segmentation. The linking model used is the hyperstack that was inspired by linear scale space theory. We discuss which properties of this paradigm are essential to determine which multiscale representations are suited as input to the hyperstack. Ifselected, one of the main problems we tackle is the estimation of the local scale such that the various stacks of images can effectively be compared. For nonlinear multiscale representations, which can be written as modiLfied diffusion equations, an upper bound can be achieved by synchronizing the evolution parametel: The synchronization is empirically veriJied by counting the number of elliptic patches at corresponding scales. We compare the resulting stacks of images and the segmentation on a test image and a coronal MR brain image.

show abstract

Probabilistic segmentation of partial volume voxels

Cited by 24 publications

References 13 publications

A graph-based framework for sub-pixel image segmentation

A graph-based framework for sub-pixel image segmentation

Measuring granule phase volume distributions using X-ray microtomography

Comparison of multiscale representations for a linking-based image segmentation model

Contact Info

Product

Resources

About