Geodesic saliency of watershed contours and hierarchical segmentation

Najman, Laurent; Schmitt, Michel

doi:10.1109/34.546254

Cited by 434 publications

(343 citation statements)

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“…Essentially, this hierarchy can be interpreted as a set of thresholded region adjacencies, wherein an ordering is determined by the value of the dynamics which are either controlled by the relative altitudes of the minima (dynamics of minima and dynamics of contours case) or by the relative heights of the watershed lines (waterfall case). A concise comparison of these approaches can be found in [3,16]. In [21], we propose an alternative to the above approaches.…”

Section: Watershed Analysis and Hierarchical Segmentationmentioning

confidence: 99%

“…This property exhibits lack of #exibility with respect to a decrease of the image region cardinality. Hierarchical approaches emanating from a Mathematical Morphology (MM) framework consist of (i) the waterfall algorithm [2], (ii) the dynamics of minima [7] and (iii) the dynamics of contours [16]. This enumeration corresponds to the chronological order of their appearance in literature.…”

Section: Watershed Analysis and Hierarchical Segmentationmentioning

confidence: 99%

“…In the sequel, we initiate the detailed discussion of the proposed scheme by describing the two algorithms which are going to be used for the contour valuation stage of our hierarchical segmentation scheme. These are the dynamics of contours [16] and the minimum cost path saliency [21].…”

Section: Watershed Analysis and Hierarchical Segmentationmentioning

confidence: 99%

“…Most of the times the complexity of the regions makes the marking procedure a very di$cult task, sometimes impossible to solve. The second trend deals with the saliency of each subpart of the watershed lines [3,16]. This subpart is the common boundary of adjacent partitions and its valuation in terms of a speci"ed saliency assigns a hierarchical structure in the image.…”

Section: Introductionmentioning

confidence: 99%

“…With respect to the salient measure module, we have taken into account two alternatives. The "rst one is the dynamics of contours [16] while the other one is the saliency of the minimum cost path curve which traverses two adjacent regions [21]. The stopping criterion module employs statistical decision via hypothesis testing which enables an automatic retrieval of the hierarchical levels for di!erent degrees of homogeneity.…”

Section: Introductionmentioning

confidence: 99%

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Low level image partitioning guided by the gradient watershed hierarchy

1999

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In this paper, we study the construction of hierarchies for gradient watersheds. The underlying analysis is based on the fact that the gradient image provides information with respect to local discontinuity and similarity. Two algorithms which utilize this type of information are analyzed. The "rst one is an extended version of the dynamics of contours and the second is the algorithm which considers the minimum cost path saliency between two adjacent regional minima. For both algorithms, a stopping criterion which de"nes and extracts automatically each hierarchical level is proposed. An important hint to determine which one of the two algorithms has a superior behavior is the domination of the pro"le type of the surface (convex or concave) formed by the gradient image. Tests are discussed on arti"cially generated and real images. The proposed bottom-up procedure provides an essential aid to the precise classi"cation of anatomical objects when used together with a user-interface environment.1999 Elsevier Science B.V. All rights reserved. ZusammenfassungIm folgenden Beitrag werden wir die Konstruktion von Hierarchien fuK r Wendepunkte untersuchen. Die zugrundeliegende Analyse beruht auf da{ Gradientenbilder Informationen uK ber lokale DiskontinuitaK ten und A G hnlichkeiten beinhalten. Es werden zwei Algorithmen untersucht, die diese Art von Information nutzen. Der erste ist eine erweiterte Version der Dynamik von Konturen und der zweite Algorithmus betrachtet minimale SpruK nge der Kostenfunktion zwischen zwei benachbarten regionalen Minima. FuK r beide Algorithmen wird ein Abbruchkriterium vorgestellt, das automatisch jede hierarchische Stufe de"niert und extrahiert. Ein wichtiger Hinweis dafuK r, welcher der beiden Algorithmen ein besseres Verhalten liefert, ist die Dominanz des Pro"ltyps der Ober#aK che (konvex oder konkav), gegeben durch das Gradientenbild. Tests mit kuK nstlichen und realen Bildern werden diskutiert. Die vorgestellte bottom-up Methode liefert eine entscheidende Hilfe zur praK zisen Klassi"zierung anatomischer Objekte, wenn sie im Zusammenhang mit einer Benutzerschnittstelle eingesetzt wird.1999 Elsevier Science B.V. All rights reserved. Re2 sume2Dans cet article, nous eH tudions la construction de hieH rarchies pour les lignes de partage des eaux du gradient. L'analyse sous-jacente repose sur le fait que l'image gradient fournit de l'information relative aux discontinuiteH s et aux similariteH s 0165-1684/99/$ -see front matter 1999 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 5 -1 6 8 4 ( 9 8 ) 0 0 2 3 2 -1 locales. Deux algorithmes qui utilisent ce type d'information sont analyseH s. Le premier est une version eH tendue de la dynamique des contours et le deuxie`me est un algorithme qui conside`re le chemin de cou( t minimum entre deux minima reH gionaux adjacents. Pour les deux algorithmes, nous proposons un crite`re d'arre( t qui deH "nit et extrait automatiquement chaque niveau hieH rarchique. Une indication importante pour deH terminer lequel des deux algorithmes a un...

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Section: Watershed Analysis and Hierarchical Segmentationmentioning

confidence: 99%

Section: Watershed Analysis and Hierarchical Segmentationmentioning

confidence: 99%

Section: Watershed Analysis and Hierarchical Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Low level image partitioning guided by the gradient watershed hierarchy

1999

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Analytical surface recognition in three-dimensional (3D) medical images using genetic matching: Application to the extraction of spheroidal articular surfaces in 3D computed tomography data sets

Jacq

Roux

Stindel

et al. 2000

Int. J. Imaging Syst. Technol.

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This paper tackles the problem of the in situ extraction of specific geometrical primitives from a three‐dimensional (3D) biomedical data set. This task involves two main problems: segmentation of major structures and extraction of the features of interest. The segmentation algorithm studied focuses on cortical bone structures. It proceeds through an analysis of a 3D watershed transform applied to the contrast image and outputs numerical surfaces as basin borders. The feature extraction task focuses on the identification of smooth regions exhibiting homogeneous curvatures, i.e., articular surfaces. We hypothesize that such surfaces can be accurately modeled through the zero set of a second‐order polynomial surface. Tracking the set of optimal parameters makes use of global and local optimization procedures, both working in the same encoding framework. The latter is a minimal subset encoding scheme. In this scheme, a model under test is indirectly described through the minimal set of data points that it interpolates, i.e., nine points in the quadratic model case. Optimization is reached by maximizing an objective function accounting for the point‐matching score of a fuzzy representation of the geometrical model vs. data points of the numerical surfaces. As such, a huge search space does not enable exhaustive exploration (e.g., the Hough transform) and the global search step makes use of a canonical genetic algorithm (i.e., a stochastic process). The latter outputs a fitness‐ordered set of solutions, which is not the best one. The subsequent local search acts as a refinement step; it performs an iterative approximation that merges some suboptimal solutions of the final‐ordered set coming from the global search. This search process is applied to the in situ extraction of spheroidal joint surfaces with the help of an ellipsoidal model. The whole algorithm is shown to be accurate and time efficient.© 2000 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 11, 30–43, 2000

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Commercial and Industrial Applications

Fong

Hui

2006

Multimedia Engineering

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Geodesic saliency of watershed contours and hierarchical segmentation

Cited by 434 publications

References 7 publications

Low level image partitioning guided by the gradient watershed hierarchy

Low level image partitioning guided by the gradient watershed hierarchy

Analytical surface recognition in three-dimensional (3D) medical images using genetic matching: Application to the extraction of spheroidal articular surfaces in 3D computed tomography data sets

Commercial and Industrial Applications

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