Stochastic Multiscale Segmentation Constrained by Image Content

Gillibert, Luc; Jeulin, Dominique

doi:10.1007/978-3-642-21569-8_12

Cited by 10 publications

(18 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To speed up the computation, merging of adjacent regions of a standard watershed can be used in the computation. When merging adjacent regions, the probability that a seed has been placed in the regions is used to compute the edge probabilities [190]. In 2014 Malmberg and Luengo Hendriks [191] presented an efficient algorithm for computing the exact stochastic watershed without any randomness.…”

Section: B Watershed Methodsmentioning

confidence: 99%

Digital Topology and Geometry in Medical Imaging: A Survey

Saha

Strand

Borgefors

2015

IEEE Trans. Med. Imaging

View full text Add to dashboard Cite

Digital topology and geometry refers to the use of topologic and geometric properties and features for images defined in digital grids. Such methods have been widely used in many medical imaging applications, including image segmentation, visualization, manipulation, interpolation, registration, surface-tracking, object representation, correction, quantitative morphometry etc. Digital topology and geometry play important roles in medical imaging research by enriching the scope of target outcomes and by adding strong theoretical foundations with enhanced stability, fidelity, and efficiency. This paper presents a comprehensive yet compact survey on results, principles, and insights of methods related to digital topology and geometry with strong emphasis on understanding their roles in various medical imaging applications. Specifically, this paper reviews methods related to distance analysis and path propagation, connectivity, surface-tracking, image segmentation, boundary and centerline detection, topology preservation and local topological properties, skeletonization, and object representation, correction, and quantitative morphometry. A common thread among the topics reviewed in this paper is that their theory and algorithms use the principle of digital path connectivity, path propagation, and neighborhood analysis.

show abstract

Section: B Watershed Methodsmentioning

confidence: 99%

Digital Topology and Geometry in Medical Imaging: A Survey

Saha

Strand

Borgefors

2015

IEEE Trans. Med. Imaging

View full text Add to dashboard Cite

show abstract

“…For multiscale images, the full granulometry of the image is used Gillibert and Jeulin (2011b). Using morphological openings, this granulometry can be automatically computed from the image and is used as a constraint during iterations of segmentation steps.…”

Section: Multiscale Image Segmentationmentioning

confidence: 99%

“…In Stawiaski and Meyer (2010) and Gillibert and Jeulin (2011b), the direct computation of the probability of the boundaries is obtained using a region adjacency graph deduced from the watershed, each vertex of the graph figuring a basin of attraction of the watershed, and each edge connecting two neigbouring basins. This graph-based approach leads to a multiscale stochastic watershed algorithm that is used now.…”

Section: Stochastic Watershedmentioning

confidence: 99%

“…A variant was introduced by the authors to operate on a wide granulometric spectrum Gillibert and Jeulin (2011b). The multiscale image segmentation process is based on a simple idea: estimate the full granulometry of the image, using morphological openings, then use multiple stochastic watersheds with different numbers of markers for each size, and finally combine them to get a segmentation that is correct for each size of grains (no oversegmentation for big grains, no undersegmentation for small grains).…”

Section: Multiscale Stochastic Watershedmentioning

confidence: 99%

See 1 more Smart Citation

3d Reconstruction and Analysis of the Fragmented Grains in a Composite Material

Gillibert¹,

Jeulin²

2013

Image Anal Stereol

Self Cite

View full text Add to dashboard Cite

X-ray microtomography from solid propellant allows studying the microstructure of fragmented grains in damaged samples. A new reconstruction algorithm of fragmented grains for 3D images is introduced. Based on a watershed transform of a morphological closing of the input image, the algorithm can be used with different sets of markers. Two of them are compared. After the grain reconstruction, a multiscale segmentation algorithm is used to extract each fragment of the damaged grains. This allows an original quantitative study of the fragmentation of each grain in 3D. Experimental results on X-ray microtomographic images of a solid propellant fragmented under compression are presented and validated.

show abstract

“…The drift of fragments in the binder phase can not be solved using a method only based on the barycenter distance, as e.g. in [11], see figures (1,6). In the first part, we present how to evaluate the shape of the interface between two objects of a given segmentation.…”

Section: Introductionmentioning

confidence: 99%

An Affinity Score for Grains Merging and Touching Grains Separation

Chabardès

Dokládal

Faessel

et al. 2017

Lecture Notes in Computer Science

View full text Add to dashboard Cite

The physical properties of granular materials on a macroscopic scale derive from their microstructures. The segmentation of CTimages of this type of material is the first step towards simulation and modeling but it is not a trivial task. Non-spherical, elongated or nonconvex objects fail to be separated with classical methods. Moreover, grains are commonly fragmented due to external conditions: aging, storage conditions, or even user-induced mechanical deformations. Grains are crushed into multiple fragments of different shape and volume; those fragments drift from one another in the binder phase. This paper focuses on reconstruction of grains from these fragments using scores that match the local thickness and the regularity of the interface between two objects from a given primary segmentation of the material. An affinity graph is built from those scores and optimized for a given application using a user-generated ground truth on a 2D slice of the tridimensional structures. A minimum spanning tree is generated, and a hierarchical cut is performed. This process allows to reassemble drifted fragments into whole grains and to solve the touching grains problem in tridimensional acquisitions.

show abstract

Stochastic Multiscale Segmentation Constrained by Image Content

Cited by 10 publications

References 4 publications

Digital Topology and Geometry in Medical Imaging: A Survey

Digital Topology and Geometry in Medical Imaging: A Survey

3d Reconstruction and Analysis of the Fragmented Grains in a Composite Material

An Affinity Score for Grains Merging and Touching Grains Separation

Contact Info

Product

Resources

About