B
B. van Ginneken
Researcher at Radboud University Nijmegen
Publications - 87
Citations - 10744
B. van Ginneken is an academic researcher from Radboud University Nijmegen. The author has contributed to research in topics: Image segmentation & Segmentation. The author has an hindex of 33, co-authored 87 publications receiving 9451 citations. Previous affiliations of B. van Ginneken include University Medical Center Utrecht & University of Iowa.
Papers
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Ridge-based vessel segmentation in color images of the retina
TL;DR: A method is presented for automated segmentation of vessels in two-dimensional color images of the retina based on extraction of image ridges, which coincide approximately with vessel centerlines, which is compared with two recently published rule-based methods.
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Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets
Tobias Heimann,B. van Ginneken,Martin Styner,Yulia Arzhaeva,V. Aurich,C. Bauer,A. Beck,C. Becker,Reinhard Beichel,G. Bekes,Fernando Bello,G. Binnig,Horst Bischof,Alexander Bornik,P. Cashman,Ying Chi,A. Cordova,Benoit M. Dawant,Marta Fidrich,Jacob D. Furst,D. Furukawa,Lars Grenacher,Joachim Hornegger,D. Kainmuller,Richard I. Kitney,H. Kobatake,Hans Lamecker,T. Lange,Jeongjin Lee,B. Lennon,Rui Li,Senhu Li,Hans-Peter Meinzer,Gábor Németh,Daniela Raicu,A.-M. Rau,E.M. van Rikxoort,Mikael Rousson,L. Rusko,K.A. Saddi,G. Schmidt,D. Seghers,Akinobu Shimizu,Pieter Slagmolen,Erich Sorantin,G. Soza,R. Susomboon,Jonathan M. Waite,A. Wimmer,Ivo Wolf +49 more
TL;DR: A comparison study between 10 automatic and six interactive methods for liver segmentation from contrast-enhanced CT images provides an insight in performance of different segmentation approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.
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Active shape model segmentation with optimal features
TL;DR: An active shape model segmentation scheme is presented that is steered by optimal local features, contrary to normalized first order derivative profiles, as in the original formulation, using a nonlinear kNN-classifier to find optimal displacements for landmarks.
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Computer analysis of computed tomography scans of the lung: a survey
TL;DR: A review of the literature on computer analysis of the lungs in CT scans is presented and addresses segmentation of various pulmonary structures, registration of chest scans, and applications aimed at detection, classification and quantification of chest abnormalities.
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Automatic detection of red lesions in digital color fundus photographs
TL;DR: A novel red lesion detection method is presented based on a hybrid approach, combining prior works by Spencer et al. (1996) and Frame (1998) with two important new contributions, including a new red lesions candidate detection system based on pixel classification.