Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Vasileios Zografos"'
Giant phyllodes tumors are rare fibroepithelial breast neoplasms typically >10 cm by definition. The best investigation for preoperative diagnosis is core biopsy, although it is often difficult for the pathologist to distinguish fibroadenomas from ph
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______2127::f5049d9dcf8c5e2e1dd7608a2ae76473
https://pergamos.lib.uoa.gr/uoa/dl/object/uoadl:3190104
https://pergamos.lib.uoa.gr/uoa/dl/object/uoadl:3190104
Publikováno v:
Computer Vision – ACCV 2016 ISBN: 9783319541808
ACCV (1)
ACCV (1)
We present an online algorithm for the efficient clustering of data drawn from a union of arbitrary dimensional, non-static subspaces. Our algorithm is based on an online min-Mahalanobis distance classifier, which simultaneously clusters and is updat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b9cf066f1aeeb21654bce93e17b7ba12
https://doi.org/10.1007/978-3-319-54181-5_23
https://doi.org/10.1007/978-3-319-54181-5_23
Publikováno v:
Medical Imaging: Image Processing
Andreasen, D, Morgenthaler Edmund, J, Zografos, V, Menze, B H & Van Leemput, K 2016, Computed tomography synthesis from magnetic resonance images in the pelvis using multiple random forests and auto-context features . in M A Styner & E D Angelini (eds), Proceedings of SPIE . vol. 9784, 978417, SPIE-International Society for Optical Engineering, SPIE Medical Imaging 2016, San Diego, California, United States, 01/03/2016 . https://doi.org/10.1117/12.2216924
Andreasen, D, Morgenthaler Edmund, J, Zografos, V, Menze, B H & Van Leemput, K 2016, Computed tomography synthesis from magnetic resonance images in the pelvis using multiple random forests and auto-context features . in M A Styner & E D Angelini (eds), Proceedings of SPIE . vol. 9784, 978417, SPIE-International Society for Optical Engineering, SPIE Medical Imaging 2016, San Diego, California, United States, 01/03/2016 . https://doi.org/10.1117/12.2216924
In radiotherapy treatment planning that is only based on magnetic resonance imaging (MRI), the electron density information usually obtained from computed tomography (CT) must be derived from the MRI by synthesizing a so-called pseudo CT (pCT). This
Autor:
Markus Rempfler, Alexander Valentinitsch, Federico Tombari, Bjoern H. Menze, Vasileios Zografos
Publikováno v:
Medical Computer Vision: Algorithms for Big Data ISBN: 9783319420158
MCV@MICCAI
MCV@MICCAI
We present a novel framework for the segmentation of multiple organs in 3D abdominal CT images, which does not require registration with an atlas. Instead we use discriminative classifiers that have been trained on an array of 3D volumetric features
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ca717b26f9ef50caf32fe74a13030877
https://doi.org/10.1007/978-3-319-42016-5_4
https://doi.org/10.1007/978-3-319-42016-5_4
Publikováno v:
Computer Vision--ACCV 2014 ISBN: 9783319168166
ACCV (4)
ACCV (4)
We present a novel approach for segmenting different motions from 3D trajectories. Our approach uses the theory of transformation groups to derive a set of invariants of 3D points located on the same rigid object. These invariants are inexpensive to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5379a6dadb292904d1e80a844af81d09
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-114313
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-114313
In this paper, we introduce a novel framework for low-level image processing and analysis. First, we process images with very simple, difference-based filter functions. Second, we fit the 2-parameter Weibull distribution to the filtered output. This
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::51a4e99c4f4314434b6e1fec730f33b2
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-90879
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-90879
Autor:
Vasileios Zografos, Reiner Lenz
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783642366994
CCIW
CCIW
We introduce a method to combine the color channels of an image to a scalar valued image. Linear combinations of the RGB channels are constructed using the Fisher-Trace-Information (FTI), defined as the trace of the Fisher information matrix of the W
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cd3a62f4b3cb75c68dc4e664ae88c199
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-89132
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-89132
Publikováno v:
Image Analysis ISBN: 9783642388859
SCIA
SCIA
In this work we derive a novel density driven diffusion scheme for image enhancement. Our approach, called D3, is a semi-local method that uses an initial structure-preserving oversegmentation step of the input image. Because of this, each segment wi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f2953fb28fe8e7741d98e1ad5cc1991f
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-90016
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-90016
Publikováno v:
Advanced Color Image Processing and Analysis ISBN: 9781441961891
This volume does much more than survey modern advanced color processing. Starting with a historical perspective on ways we have classified color, it sets out the latest numerical techniques for ana ...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::efda14e8cb8c5bbf2ff5f9d56833c32c
https://doi.org/10.1007/978-1-4419-6190-7_5
https://doi.org/10.1007/978-1-4419-6190-7_5
Autor:
Liam Ellis, Vasileios Zografos
Publikováno v:
Computer Vision – ACCV 2012 ISBN: 9783642374432
ACCV (2)
ACCV (2)
This work addresses the problem of fast, online segmentationof moving objects in video. We pose this as a discriminative onlinesemi-supervised appearance learning task, where supervising labelsare autonomously generated by a motion segmentation algor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c798adc49ad85290ef64e1960e66b189
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-86211
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-86211