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» Analysis of Functional Magnetic Resonance Imaging in Python
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ICML
2006
IEEE
14 years 8 months ago
Hidden process models
We introduce Hidden Process Models (HPMs), a class of probabilistic models for multivariate time series data. The design of HPMs has been motivated by the challenges of modeling h...
Rebecca Hutchinson, Tom M. Mitchell, Indrayana Rus...
ICIP
2006
IEEE
14 years 9 months ago
Techniques for Fusion of Multimodal Images: Application to Breast Imaging
In many situations it is desirable and advantageous to acquire medical images in more than one modality. For example positron emission tomography can be used to acquire functional...
Andrzej Król, David H. Feiglin, Ethan D. Mo...
CIMAGING
2008
172views Hardware» more  CIMAGING 2008»
13 years 9 months ago
Blind reconstruction of sparse images with unknown point spread function
We consider the image reconstruction problem when the original image is assumed to be sparse and when partial knowledge of the point spread function (PSF) is available. In particu...
Kyle Herrity, Raviv Raich, Alfred O. Hero III
TMI
2010
182views more  TMI 2010»
13 years 6 months ago
A Bayesian Mixture Approach to Modeling Spatial Activation Patterns in Multisite fMRI Data
Abstract—We propose a probabilistic model for analyzing spatial activation patterns in multiple functional magnetic resonance imaging (fMRI) activation images such as repeated ob...
Seyoung Kim, Padhraic Smyth, Hal S. Stern
RECOMB
2001
Springer
14 years 3 hour ago
Extracting structural information using time-frequency analysis of protein NMR data
High-throughput, data-directed computational protocols for Structural Genomics (or Proteomics) are required in order to evaluate the protein products of genes for structure and fu...
Christopher James Langmead, Bruce Randall Donald