Zobrazeno 1 - 10
of 97
pro vyhledávání: '"Sebastian Kurtek"'
Autor:
Yi Tang Chen, Sebastian Kurtek
Publikováno v:
Data Science in Science, Vol 3, Iss 1 (2024)
We use a geometric approach to jointly characterize tumor shape and intensity along the tumor contour, as captured in magnetic resonance images, in the context of glioblastoma multiforme. Key properties of the proposed shape + intensity representatio
Externí odkaz:
https://doaj.org/article/71c749c268c24afc88c76d1de5ea8243
Publikováno v:
PLoS ONE, Vol 18, Iss 7, p e0287734 (2023)
In this work, we develop a new set of Bayesian models to perform registration of real-valued functions. A Gaussian process prior is assigned to the parameter space of time warping functions, and a Markov chain Monte Carlo (MCMC) algorithm is utilized
Externí odkaz:
https://doaj.org/article/22335d13ad6745f6996871aab866c512
Autor:
Gregory J. Matthews, Karthik Bharath, Sebastian Kurtek, Juliet K. Brophy, George K. Thiruvathukal, Ofer Harel
Publikováno v:
Frontiers in Applied Mathematics and Statistics, Vol 7 (2021)
We consider the problem of classifying curves when they are observed only partially on their parameter domains. We propose computational methods for (i) completion of partially observed curves; (ii) assessment of completion variability through a nonp
Externí odkaz:
https://doaj.org/article/65624c01bd0942af928af37e7bfb400d
Autor:
Abhijoy Saha, Sayantan Banerjee, Sebastian Kurtek, Shivali Narang, Joonsang Lee, Ganesh Rao, Juan Martinez, Karthik Bharath, Arvind U.K. Rao, Veerabhadran Baladandayuthapani
Publikováno v:
NeuroImage: Clinical, Vol 12, Iss C, Pp 132-143 (2016)
Tumor heterogeneity is a crucial area of cancer research wherein inter- and intra-tumor differences are investigated to assess and monitor disease development and progression, especially in cancer. The proliferation of imaging and linked genomic data
Externí odkaz:
https://doaj.org/article/df69e0727bf54758871b8b0b9de7ae15
Publikováno v:
Wiley StatsRef: Statistics Reference Online. :1-15
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::971273221c243898aa3b490bbfbe176f
https://doi.org/10.1002/9781118445112.stat08265
https://doi.org/10.1002/9781118445112.stat08265
Publikováno v:
J Multivar Anal
It is quite common for functional data arising from imaging data to assume values in infinite-dimensional manifolds. Uncovering associations between two or more such nonlinear functional data extracted from the same object across medical imaging moda
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::419de107e5244302e42bba539521db81
https://nottingham-repository.worktribe.com/file/6537009/1/TFCCA
https://nottingham-repository.worktribe.com/file/6537009/1/TFCCA
Publikováno v:
IEEE Trans Vis Comput Graph
We propose a new method for the construction and visualization of geometrically-motivated boxplot displays for elastic curve data. We use a recent shape analysis framework, based on the square-root velocity function representation of curves, to extra
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::53b783eed57eb61743d845b43a20e56b
https://doi.org/10.1109/tvcg.2019.2921541
https://doi.org/10.1109/tvcg.2019.2921541
Autor:
Shariq Mohammed, Karthik Bharath, Sebastian Kurtek, Arvind Rao, Veerabhadran Baladandayuthapani
Recent technological advancements have enabled detailed investigation of associations between the molecular architecture and tumor heterogeneity, through multi-source integration of radiological imaging and genomic (radiogenomic) data. In this paper,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::80783aba816962f608e72d43543ef5a5
Publikováno v:
Spatial Statistics. 51:100687
Spatial, amplitude and phase variations in spatial functional data are confounded. Conclusions from the popular functional trace-variogram, which quantifies spatial variation, can be misleading when analyzing misaligned functional data with phase var
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7e7c8e2c6dd22e71dd8bd41216c368bc
https://doi.org/10.1016/j.spasta.2022.100687
https://doi.org/10.1016/j.spasta.2022.100687
Publikováno v:
CVPR Workshops
Functional data analysis (FDA) is focused on various statistical tasks, including inference, for observations that vary over a continuum, which are not effectively addressed by multivariate methods. A feature of these functional observations is the p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6c215a903231440a5e76e68f17057d60
https://doi.org/10.1109/cvprw53098.2021.00500
https://doi.org/10.1109/cvprw53098.2021.00500