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Norberto Malpica

Researcher at King Juan Carlos University

Publications -  92
Citations -  3338

Norberto Malpica is an academic researcher from King Juan Carlos University. The author has contributed to research in topics: Segmentation & Image segmentation. The author has an hindex of 20, co-authored 88 publications receiving 2428 citations. Previous affiliations of Norberto Malpica include National University of Distance Education & ETSI.

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Variability in the analysis of a single neuroimaging dataset by many teams

Rotem Botvinik-Nezer, +220 more
- 04 Jun 2020 - 
TL;DR: The results obtained by seventy different teams analysing the same functional magnetic resonance imaging dataset show substantial variation, highlighting the influence of analytical choices and the importance of sharing workflows publicly and performing multiple analyses.
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Applying watershed algorithms to the segmentation of clustered nuclei

TL;DR: An algorithm based on morphological watersheds has been implemented and tested on the segmentation of microscopic nuclei clusters and provides a tool that can be used for the implementation of both gradient- and domain-based algorithms, and, more importantly, for the Implementation of mixed (gradient- and shape-based) algorithms.
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Evaluation of autofocus functions in molecular cytogenetic analysis

TL;DR: A systematic evaluation of several autofocus functions used for analytical fluorescent image cytometry studies of counterstained nuclei shows that functions based on correlation measures have the best performance for this type of image.
Posted ContentDOI

Variability in the analysis of a single neuroimaging dataset by many teams (Preprint)

Rotem Botvinik-Nezer, +196 more
TL;DR: In this paper, the same dataset was independently analyzed by 70 teams, testing nine ex-ante hypotheses, and the results showed that analytic flexibility can have substantial effects on scientific conclusions, and demonstrate factors related to variability in fMRI.
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Single-image super-resolution of brain MR images using overcomplete dictionaries

TL;DR: A sparse-based super-resolution method, adapted for easily including prior knowledge, which couples up high and low frequency information so that a high-resolution version of a low-resolution brain MR image is generated, shown to outperform a recent state-of-the-art algorithm.