D
Dimitris Maroulis
Researcher at National and Kapodistrian University of Athens
Publications - 131
Citations - 2366
Dimitris Maroulis is an academic researcher from National and Kapodistrian University of Athens. The author has contributed to research in topics: Image segmentation & Active contour model. The author has an hindex of 25, co-authored 131 publications receiving 2173 citations. Previous affiliations of Dimitris Maroulis include Athens State University.
Papers
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Proceedings ArticleDOI
Autopilot spatially-adaptive active contour parameterization for medical image segmentation
TL;DR: The segmentation results demonstrate that the proposed framework bypasses iterations dedicated to false local minima associated with noise, artifacts and inhomogeneities, speeding up contour convergence, whereas it maintains a high segmentation quality.
Proceedings ArticleDOI
An automatically initialized level-set approach for the segmentation of proteomics images
TL;DR: The experimental results indicate that the proposed level-set approach facilitates quicker convergence than the one obtained by the straightforward application of the Chan-Vese model, and the identified spot boundaries are more plausible than the ones obtain by the application of state-of-the-art proteomics image analysis software.
Journal ArticleDOI
Control System Implementation for the IASA Microtron
M. Barbarosou,T. A. Filippas,E. N. Gazis,A. Karabarbounis,Dimitris Maroulis,Costas N. Papanicolas,N. Patavalis,H. Rahmani +7 more
TL;DR: A progress report on the architectural design and implementation of the Control System for the Racetrack Microtron at the Institute of Accelerating Systems and Applications in Athens, Greece is presented.
Proceedings ArticleDOI
Unsupervised level set parameterization using multi-scale filtering
Eleftheria A. Mylona,Michalis A. Savelonas,Eleni Zacharia,Dimitris Maroulis,Constantinos S. Pattichis +4 more
TL;DR: The experimental results demonstrate that the proposed framework is capable of accelerating contour convergence, whereas it obtains a segmentation quality comparable to the one obtained with empirically optimized parameterization.
Book ChapterDOI
Antibody Clustering Using a Machine Learning Pipeline that Fuses Genetic, Structural, and Physicochemical Properties.
Louis Papageorgiou,Dimitris Maroulis,George P. Chrousos,Elias Eliopoulos,Dimitrios Vlachakis +4 more
TL;DR: A new simple philosophy is proposed to transform the conserved framework of antibody V domain in a binary form using structural features of antibody-antigen interactions, toward identifying new antibody signatures in V domain binding activity.