Seed-Relative Segmentation Robustness of Watershed and Fuzzy Connectedness Approaches

Leotta, Matthew J.; Vandergon, Austin; Taubin, Gabriel

doi:10.1109/sibgrapi.2007.26

Cited by 25 publications

(18 citation statements)

References 8 publications

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“…Similar important relations can be obtained with other image operators, such as relative-fuzzy connected segmentation [18][19][20][21]. In our case, the markers are the prototypes and we have a special way to estimate them.…”

Section: Introductionsupporting

confidence: 53%

Supervised pattern classification based on optimum‐path forest

Papa

Falcão

Suzuki

2009

Int J Imaging Syst Tech

350

254

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We present a supervised classification method which represents each class by one or more optimum-path trees rooted at some key samples, called prototypes. The training samples are nodes of a complete graph, whose arcs are weighted by the distances between the feature vectors of their nodes. Prototypes are identified in all classes and the minimization of a connectivity function by dynamic programming assigns to each training sample a minimumcost path from its most strongly connected prototype. This competition among prototypes partitions the graph into an optimum-path forest rooted at them. The class of the samples in an optimum-path tree is assumed to be the same of its root. A test sample is classified similarly, by identifying which tree would contain it, if the sample were part of the training set. By choice of the graph model and connectivity function, one can devise other optimum-path forest classifiers. We present one of them, which is fast, simple, multi-class, parameter independent, does not make any assumption about the shapes of the classes, and can handle some degree of overlapping between classes. We also propose a general algorithm to learn from errors on an evaluation set without increasing the training set, and show the advantages of our method with respect to SVM, ANN-MLP, and k-NN classifiers in several experiments with datasets of various types.

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Section: Introductionsupporting

confidence: 53%

Supervised pattern classification based on optimum‐path forest

Papa

Falcão

Suzuki

2009

Int J Imaging Syst Tech

350

254

View full text Add to dashboard Cite

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“…In [17], we have proved the equivalence between MSF rooted in the set of minima and watershed cuts. In practice, watersheds from markers are often computed, and subsets of minima of the original edge-weighted graph constitute robust markers [23]. The next definition, illustrated in Figs.…”

Section: Minimum Spanning Forests Hierarchiesmentioning

confidence: 99%

Incremental Algorithm for Hierarchical Minimum Spanning Forests and Saliency of Watershed Cuts

Cousty

Najman

2011

Mathematical Morphology and Its Applications to Image and Signal Processing

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Abstract. We study hierarchical segmentations that are optimal in the sense of minimal spanning forests of the original image. We introduce a region-merging operation called uprooting, and we prove that optimal hierarchical segmentations are equivalent to the ones given by uprooting a watershed-cut based segmentation. Based on those theoretical results, we propose an efficient algorithm to compute such hierarchies, as well as the first saliency map algorithm compatible with the morphological filtering framework.

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“…After that, the seed's labels are propagated to all unlabeled regions by following some optimum criterion, such that a complete labeled image is constructed. This class encloses many of the most prominent methods for general purpose segmentation, which are usually easier to extend to multi-dimensional images, including frameworks, such as watershed from markers [6,7], random walks [8], fuzzy connectedness [9,10], graph cuts (GC) [11], distance cut [12], image foresting transform [13], and grow cut [14]. The study of the relations among different frameworks, including theoretical and empirical comparisons, has a vast literature [15][16][17][18][19], which allowed many algorithms to be described in a unified manner according to a common framework, which we refer to as generalized GC (GGC) [18,20].…”

Section: Introductionmentioning

confidence: 99%

“…RFC is an important method, which presents some nice theoretical properties, such as the robustness with respect to the seed choice [26]. The regions where the seeds are free to move without affecting the segmentation are called in some works as the cores [10]. In RFC, the cores for each seed coincide with its corresponding delineated regions by RFC.…”

Section: Introductionmentioning

confidence: 99%

“…In RFC, the cores for each seed coincide with its corresponding delineated regions by RFC. The cores of RFC are key elements in theoretical analysis to support effective semi-automatic correction (i.e., to fix a poor automatic segmentation in an interactive tool [27,28]), by finding a suitable set of seeds that assembles a given segmentation [10]. The RFC also has the advantage of producing a low false positive rate, which allows it to be combined with other methods in powerful hybrid approaches [29,30].…”

Section: Introductionmentioning

confidence: 99%

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Oriented relative fuzzy connectedness: theory, algorithms, and its applications in hybrid image segmentation methods

Bejar

Miranda

2015

J Image Video Proc.

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Anatomical structures and tissues are often hard to be segmented in medical images due to their poorly defined boundaries, i.e., low contrast in relation to other nearby false boundaries. The specification of the boundary polarity can help alleviate a part of this problem. In this work, we discuss how to incorporate this property in the relative fuzzy connectedness (RFC) framework. We include a theoretical proof of the optimality of the new algorithm, named oriented relative fuzzy connectedness (ORFC), in terms of an oriented energy function subject to the seed constraints, and show its usage to devise powerful hybrid image segmentation methods. The methods are evaluated using medical images of MRI and CT of the human brain and thoracic studies.

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Seed-Relative Segmentation Robustness of Watershed and Fuzzy Connectedness Approaches

Abstract: Abstract

Cited by 25 publications

References 8 publications

Supervised pattern classification based on optimum‐path forest

Supervised pattern classification based on optimum‐path forest

Incremental Algorithm for Hierarchical Minimum Spanning Forests and Saliency of Watershed Cuts

Oriented relative fuzzy connectedness: theory, algorithms, and its applications in hybrid image segmentation methods

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