Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Martin S. Silbiger"'
Autor:
Lawrence O. Hall, Reed Murtagh, R.P. Velthuizen, M.C. Clark, Dimitry B. Goldgof, Martin S. Silbiger
Publikováno v:
FUZZY and NEURO-FUZZY SYSTEMS in MEDICINE ISBN: 9780203713419
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::74ae4198bfb7c5c1f601d275c8c82cc0
https://doi.org/10.1201/9780203713419-6
https://doi.org/10.1201/9780203713419-6
Autor:
Harvey Greenberg, M. Vaidyanathan, Lawrence O. Hall, James C. Bezdek, Laurence P. Clarke, A. Trotti, Martin S. Silbiger, A. Bensaid, R.P. Velthuizen, S. Phuphanich
Publikováno v:
Magnetic Resonance Imaging. 13:719-728
Two different multispectral pattern recognition methods are used to segment magnetic resonance images (MRI) of the brain for quantitative estimation of tumor volume and volume changes with therapy. A supervised k-nearest neighbor (kNN) rule and a sem
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c83f898659115a9ed2f6cdd362c5b96d
https://doi.org/10.1016/0730-725x(95)00012-6
https://doi.org/10.1016/0730-725x(95)00012-6
Autor:
Dmitry B. Goldgof, Laurence P. Clarke, Martin S. Silbiger, M.C. Clark, R.P. Velthuizen, Lawrence O. Hall
Publikováno v:
IEEE Engineering in Medicine and Biology Magazine. 13:730-742
The authors' main contribution is to build upon their earlier efforts by expanding the tissue model concept to cover a brain volume. Furthermore, processing time is reduced and accuracy is enhanced by the use of knowledge propagation, where informati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f0264463970a4b13989962d4c2190e73
https://doi.org/10.1109/51.334636
https://doi.org/10.1109/51.334636
Autor:
S. Phuphanich, Laurence P. Clarke, R.P. Velthuizen, J.D. Schellenberg, Martin S. Silbiger, John A. Arrington
Publikováno v:
Magnetic Resonance Imaging. 11:95-106
Supervised segmentation methods from three families of pattern recognition techniques were used to segment multispectral MRI data. Studied were the maximum likelihood method (MLM), k-nearest neighbors (k-NN), and a back-propagation artificial neural
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a202e76cad383f7b9e2f54398a6ac824
https://doi.org/10.1016/0730-725x(93)90417-c
https://doi.org/10.1016/0730-725x(93)90417-c
Autor:
R.P. Velthuizen, Lawrence O. Hall, Martin S. Silbiger, Dmitry B. Goldgof, F.R. Murtagh, M.C. Clark
Publikováno v:
IEEE transactions on medical imaging. 17(2)
A system that automatically segments and labels glioblastoma-multiforme tumors in magnetic resonance images (MRI's) of the human brain is presented. The MRI's consist of T1-weighted, proton density, and T2-weighted feature images and are processed by
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fa8e3b6e13c3f84677e5dd65569b4bcd
https://pubmed.ncbi.nlm.nih.gov/9688151
https://pubmed.ncbi.nlm.nih.gov/9688151
Publikováno v:
Fuzzy Logic in Artificial Intelligence Towards Intelligent Systems ISBN: 9783540624745
Fuzzy Logic in Artificial Intelligence
Fuzzy Logic in Artificial Intelligence
This paper presents a system that integrates a knowledge-based system with unsupervised fuzzy clustering to automatically segment and label glioblastoma multiforme tumors in magnetic resonance slices of the human brain. Each slice is initially segmen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::40ca9b8f7104dbf17b818c043e54ed39
https://doi.org/10.1007/3-540-62474-0_13
https://doi.org/10.1007/3-540-62474-0_13
Publikováno v:
Fuzzy Logic and Soft Computing
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::240f55f7d4d73344e7b0b91a52fc973a
https://doi.org/10.1142/9789812830753_0010
https://doi.org/10.1142/9789812830753_0010
Autor:
James C. Bezdek, R.P. Velthuizen, Lawrence O. Hall, Laurence P. Clarke, Martin S. Silbiger, A. Bensaid
Publikováno v:
IEEE transactions on neural networks. 3(5)
Magnetic resonance (MR) brain section images are segmented and then synthetically colored to give visual representations of the original data with three approaches: the literal and approximate fuzzy c-means unsupervised clustering algorithms, and a s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3ab6151a308539337fb26c44f2d1a09e
https://pubmed.ncbi.nlm.nih.gov/18276467
https://pubmed.ncbi.nlm.nih.gov/18276467
Autor:
Martin S. Silbiger, C.L. Partain, A. Everette James, T. Greeson, R.J. Hamilton, John C. Gore, S. Baum
Publikováno v:
Magnetic resonance imaging. 5(1)
A significant proportion of MRI units are being installed in MRI Centers that are free standing enterprises offering outpatient diagnoses separate from hospitals. The development of such Centers represents a challenge to more traditional arrangements
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::42914840c4fa08b8b590ba4912c4fab2
https://pubmed.ncbi.nlm.nih.gov/3586875
https://pubmed.ncbi.nlm.nih.gov/3586875