Fabio A. M. Cappabianco

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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),(More)
Although white matter damage may play a major role in the pathogenesis of spinocerebellar ataxia 3 (SCA3), available data rely exclusively upon macrostructural analyses. In this setting we designed a study to investigate white matter integrity. We evaluated 38 genetically-confirmed SCA3 patients (mean age, 52.76 ± 12.70 years; 21 males) with clinical scales(More)
A new approach to identify clusters as trees of an optimum-path forest has been presented. We are extending the method for large datasets with application to automatic GM/WM classification in MR-T1 images of the brain. The method is computed for a few randomly selected voxels, such that GM and WM define two optimum-path trees. The remaining voxels are(More)
—Traditional pattern recognition techniques can not handle the classification of large datasets with both efficiency and effectiveness. In this context, the Optimum-Path Forest (OPF) classifier was recently introduced, trying to achieve high recognition rates and low computational cost. Although OPF was much faster than Support Vector Machines for training,(More)
We present an accurate and fast approach for MR-image segmentation of brain tissues, that is robust to anatomical variations and takes an average of less than one minute for completion on modern PCs. The method first corrects voxel values in the brain based on local estimations of the white-matter intensities. This strategy is inspired by other works, but(More)
—Image segmentation, such as to extract an object from a background, is very useful for medical and biological image analysis. In this work, we propose new methods for interactive segmentation of multidimensional images, based on the Image Foresting Transform (IFT), by exploiting for the first time non-smooth connectivity functions (NSCF) with a strong(More)
Fig. 1. Teasing the need for inhomogeneity correction prior to skull stripping in high magnetic resonance field images (e.g. 3 Tesla). Left: Segmentation made by a popular automatic method. Right: Improved skull stripping applying a novel proposed iterative inhomogeneity correction methodology. Abstract—Bias field (inhomogeneity) correction and skull(More)