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Nikos Paragios

Researcher at Université Paris-Saclay

Publications -  374
Citations -  23281

Nikos Paragios is an academic researcher from Université Paris-Saclay. The author has contributed to research in topics: Segmentation & Image segmentation. The author has an hindex of 62, co-authored 349 publications receiving 20737 citations. Previous affiliations of Nikos Paragios include École Centrale Paris & University of Crete.

Papers
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Journal ArticleDOI

Deformable Medical Image Registration: A Survey

Aristeidis Sotiras, +2 more
- 03 Jun 2013 - 
TL;DR: This paper attempts to give an overview of deformable registration methods, putting emphasis on the most recent advances in the domain, and provides an extensive account of registration techniques in a systematic manner.
Posted ContentDOI

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Spyridon Bakas, +438 more
- 05 Nov 2018 - 
TL;DR: This study assesses the state-of-the-art machine learning methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018, and investigates the challenge of identifying the best ML algorithms for each of these tasks.
Journal ArticleDOI

Geodesic active contours and level sets for the detection and tracking of moving objects

Nikos Paragios, +1 more
- 01 Mar 2000 - 
TL;DR: A new approach named Hermes is proposed, which exploits aspects from the well-known front propagation algorithms and compares favorably to them, and very promising experimental results are provided using real video sequences.
Book

"Geometric Level Set Methods in Imaging, Vision, and Graphics"

Stanley Osher, +1 more
TL;DR: This book discusses methods for preserving geometric deformable models for brain reconstruction, as well as methods for implicit active contour models, and some of the methods used in this book were adapted for this purpose.
Journal ArticleDOI

Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation

Nikos Paragios, +1 more
- 11 Feb 2002 - 
TL;DR: A novel variational framework to deal with frame partition problems in Computer Vision that exploits boundary and region-based segmentation modules under a curve-based optimization objective function is presented.