Clustering by optimum path forest and its application to automatic GM/WM classification in MR-T1 images of the brain

Cappabianco, Fábio A. M.; Falcão, Alexandre X.; Rocha, Leonardo Marques

doi:10.1109/isbi.2008.4541024

Cited by 12 publications

(17 citation statements)

References 14 publications

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“…The OPF classifiers are being successfully used in some real applications: the supervised approach is being used for oropharyngeal dysphagia identification [37], laryngeal pathology detection [36], and diagnosis of parasites from optical microscopy images [38], and the unsupervised approach is being used for the separation of grey-matter and white-matter in Magnetic Resonance images of the brain [24]. In the first three applications, the supervised OPF outperforms SVM in accuracy and efficiency.…”

Section: Discussionmentioning

confidence: 99%

“…As far as we know, our approach is the first to consider optimum-path forests rooted at automatically selected prototypes in the feature space. Besides, by changing the graph model and path-value function, one can derive other types of optimum-path forest classifiers, such as the unsupervised learning approach proposed in [23,24], which also relies on a different strategy to estimate prototypes. Most approaches for pattern classification based on graphs and/or paths in graphs are either unsupervised [25][26][27][28] or semi-supervised [29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

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Supervised pattern classification based on optimum‐path forest

Papa

Falcão

Suzuki

2009

Int J Imaging Syst Tech

366

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: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Supervised pattern classification based on optimum‐path forest

Papa

Falcão

Suzuki

2009

Int J Imaging Syst Tech

366

254

View full text Add to dashboard Cite

show abstract

“…Our method differs from that in the graph model, connectivity function, learning algorithm, and application, which is in our case, unsupervised. Previous versions of our work have also been published (Cappabianco et al, 2008;. The present article merges and extends them by improving methods and results for large datasets, such as images.…”

Section: Introductionmentioning

confidence: 86%

“…It represents an advance with respect to our previous approach (Cappabianco et al, 2008), which did not use neither inhomogeneity reduction nor majority vote. B.1.…”

Section: Dataset (Nclasses)mentioning

confidence: 98%

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Data clustering as an optimum‐path forest problem with applications in image analysis

Rocha

Cappabianco

Falcão

2009

Int J Imaging Syst Tech

125

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ABSTRACT:We propose an approach for data clustering based on optimum-path forest. The samples are taken as nodes of a graph, whose arcs are defined by an adjacency relation. The nodes are weighted by their probability density values (pdf) and a connectivity function is maximized, such that each maximum of the pdf becomes root of an optimum-path tree (cluster), composed by samples ''more strongly connected'' to that maximum than to any other root. We discuss the advantages over other pdf-based approaches and present extensions to large datasets with results for interactive image segmentation and for fast, accurate, and automatic brain tissue classification in magnetic resonance (MR) images. We also include experimental comparisons with other clustering approaches.

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