Bayesian estimation of hyperparameters in MRI through the maximum evidence method
Bayesian inference methods are commonly applied to the classification of brain Magnetic Resonance images (MRI). We use the Maximum Evidence (ME) approach to estimate the most probable parameters and hyperparameters for models that take into account discrete classes (DM) and models accounting for the...
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| Acceso en línea: | http://hdl.handle.net/20.500.12110/paper_97807695_v_n_p129_Oliva |
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todo:paper_97807695_v_n_p129_Oliva2023-10-03T16:42:49Z Bayesian estimation of hyperparameters in MRI through the maximum evidence method Oliva, D.E. Isoardi, R.A. Mato, G. Bayesian networks Color image processing Computational geometry Computer graphics Digital image storage Image enhancement Image processing Imaging systems Magnetic resonance imaging Parameter estimation Resonance And models Approximate algorithms Bayesian estimations Bayesian inferences Computationally expensive Digital phantoms Error predictions Hyper parameters Magnetic resonance images Measured datums Model optimizations Partial volume effects Simulated images Inference engines Bayesian inference methods are commonly applied to the classification of brain Magnetic Resonance images (MRI). We use the Maximum Evidence (ME) approach to estimate the most probable parameters and hyperparameters for models that take into account discrete classes (DM) and models accounting for the partial volume effect (PVM). An approximate algorithm was developed for model optimization, since the exact image inference calculation is computationally expensive. The method was validated using simulated images and a digital phantom. We show that the Evidence is a very useful figure for error prediction, which is to be maximized respect to the hyperparameters. Additionally, it provides a tool to determine the most probable model given measured data. © 2008 IEEE. CONF info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_97807695_v_n_p129_Oliva |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
| collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
Bayesian networks Color image processing Computational geometry Computer graphics Digital image storage Image enhancement Image processing Imaging systems Magnetic resonance imaging Parameter estimation Resonance And models Approximate algorithms Bayesian estimations Bayesian inferences Computationally expensive Digital phantoms Error predictions Hyper parameters Magnetic resonance images Measured datums Model optimizations Partial volume effects Simulated images Inference engines |
| spellingShingle |
Bayesian networks Color image processing Computational geometry Computer graphics Digital image storage Image enhancement Image processing Imaging systems Magnetic resonance imaging Parameter estimation Resonance And models Approximate algorithms Bayesian estimations Bayesian inferences Computationally expensive Digital phantoms Error predictions Hyper parameters Magnetic resonance images Measured datums Model optimizations Partial volume effects Simulated images Inference engines Oliva, D.E. Isoardi, R.A. Mato, G. Bayesian estimation of hyperparameters in MRI through the maximum evidence method |
| topic_facet |
Bayesian networks Color image processing Computational geometry Computer graphics Digital image storage Image enhancement Image processing Imaging systems Magnetic resonance imaging Parameter estimation Resonance And models Approximate algorithms Bayesian estimations Bayesian inferences Computationally expensive Digital phantoms Error predictions Hyper parameters Magnetic resonance images Measured datums Model optimizations Partial volume effects Simulated images Inference engines |
| description |
Bayesian inference methods are commonly applied to the classification of brain Magnetic Resonance images (MRI). We use the Maximum Evidence (ME) approach to estimate the most probable parameters and hyperparameters for models that take into account discrete classes (DM) and models accounting for the partial volume effect (PVM). An approximate algorithm was developed for model optimization, since the exact image inference calculation is computationally expensive. The method was validated using simulated images and a digital phantom. We show that the Evidence is a very useful figure for error prediction, which is to be maximized respect to the hyperparameters. Additionally, it provides a tool to determine the most probable model given measured data. © 2008 IEEE. |
| format |
CONF |
| author |
Oliva, D.E. Isoardi, R.A. Mato, G. |
| author_facet |
Oliva, D.E. Isoardi, R.A. Mato, G. |
| author_sort |
Oliva, D.E. |
| title |
Bayesian estimation of hyperparameters in MRI through the maximum evidence method |
| title_short |
Bayesian estimation of hyperparameters in MRI through the maximum evidence method |
| title_full |
Bayesian estimation of hyperparameters in MRI through the maximum evidence method |
| title_fullStr |
Bayesian estimation of hyperparameters in MRI through the maximum evidence method |
| title_full_unstemmed |
Bayesian estimation of hyperparameters in MRI through the maximum evidence method |
| title_sort |
bayesian estimation of hyperparameters in mri through the maximum evidence method |
| url |
http://hdl.handle.net/20.500.12110/paper_97807695_v_n_p129_Oliva |
| work_keys_str_mv |
AT olivade bayesianestimationofhyperparametersinmrithroughthemaximumevidencemethod AT isoardira bayesianestimationofhyperparametersinmrithroughthemaximumevidencemethod AT matog bayesianestimationofhyperparametersinmrithroughthemaximumevidencemethod |
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1782031095912464384 |