W
Wellington Pinheiro dos Santos
Researcher at Federal University of Pernambuco
Publications - 143
Citations - 1603
Wellington Pinheiro dos Santos is an academic researcher from Federal University of Pernambuco. The author has contributed to research in topics: Electrical impedance tomography & Iterative reconstruction. The author has an hindex of 16, co-authored 125 publications receiving 1037 citations. Previous affiliations of Wellington Pinheiro dos Santos include Federal University of Campina Grande & Universidade de Pernambuco.
Papers
More filters
Journal ArticleDOI
Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure
Olivier Commowick,Audrey Istace,Michael Kain,Baptiste Laurent,Florent Leray,Mathieu Simon,Sorina Camarasu-Pop,Pascal Girard,Roxana Ameli,Jean-Christophe Ferré,Anne Kerbrat,Thomas Tourdias,Frederic Cervenansky,Tristan Glatard,Jeremy Beaumont,Senan Doyle,Florence Forbes,Jesse Knight,April Khademi,Amirreza Mahbod,Chunliang Wang,Richard McKinley,Franca Wagner,John Muschelli,Elizabeth M. Sweeney,Eloy Roura,Xavier Lladó,Michel M. dos Santos,Wellington Pinheiro dos Santos,Abel G. Silva-Filho,Xavier Tomas-Fernandez,Hélène Urien,Isabelle Bloch,Sergi Valverde,Mariano Cabezas,Francisco Javier Vera-Olmos,Norberto Malpica,Charles R.G. Guttmann,Sandra Vukusic,Gilles Edan,Michel Dojat,Martin Styner,Simon K. Warfield,François Cotton,Christian Barillot +44 more
TL;DR: Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods, are still trailing human expertise on both detection and delineation criteria, and it is demonstrated that computing a statistically robust consensus of the algorithms performs closer tohuman expertise on one score (segmentation) although still trailing on detection scores.
Posted ContentDOI
Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure
Olivier Commowick,Audrey Istace,Michael Kain,Baptiste Laurent,Florent Leray,Mathieu Simon,Sorina Camarasu-Pop,Pascal Girard,Roxana Ameli,Jean-Christophe Ferré,Anne Kerbrat,Thomas Tourdias,Frederic Cervenansky,Tristan Glatard,Jeremy Beaumont,Senan Doyle,Florence Forbes,Jesse Knight,April Khademi,Amirreza Mahbod,Chunliang Wang,Richard McKinley,Franca Wagner,John Muschelli,Elizabeth M. Sweeney,Eloy Roura,Xavier Lladó,Michel M. dos Santos,Wellington Pinheiro dos Santos,Abel G. Silva-Filho,Xavier Tomas-Fernandez,Hélène Urien,Isabelle Bloch,Sergi Valverde,Mariano Cabezas,Francisco Javier Vera-Olmos,Norberto Malpica,Charles R.G. Guttmann,Sandra Vukusic,Gilles Edan,Michel Dojat,Martin Styner,Simon K. Warfield,François Cotton,Christian Barillot +44 more
TL;DR: Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods, are still trailing human expertise on both detection and delineation criteria, and it is demonstrated that computing a statistically robust consensus of the algorithms performs closer tohuman expertise on one score (segmentation) although still trailing on detection scores.
Journal ArticleDOI
Breast cancer diagnosis based on mammary thermography and extreme learning machines
Maíra Araújo de Santana,Jessiane Mônica Silva Pereira,Fabrício Lucimar da Silva,Nigel Mendes de Lima,Felipe Nunes de Sousa,Guilherme Max Silva de Arruda,Rita de Cássia Fernandes de Lima,Washington Wagner Azevedo da Silva,Wellington Pinheiro dos Santos +8 more
TL;DR: Extreme Learning Machines (ELM) and Multilayer Perceptron networks (MLP) proved to be quite efficient classifiers for classification of breast lesions in thermographic images.
Journal ArticleDOI
Detection and classification of masses in mammographic images in a multi-kernel approach
TL;DR: A method to detect and classify mammographic lesions using the regions of interest of images using multi-resolution wavelets and Zernike moments, which can combine both texture and shape features, which is 50 times superior to state-of-the-art approaches.
Journal ArticleDOI
A semi-supervised fuzzy GrowCut algorithm to segment and classify regions of interest of mammographic images
TL;DR: A new semi-supervised segmentation algorithm based on the modification of the GrowCut algorithm to perform automatic mammographic image segmentation once a region of interest is selected by a specialist, being robust and as efficient as state of the art techniques.