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Title:

On the Use of Local RBF Networks to Approximate Multivalued Functions and Relations

Description:

A connectionist model made up of a combination of RBF networks is proposed; the model decomposes multivalued dependencies into local single valued functions; theory and applications are presented. 1 Introduction We present a new network structure which is modelled according to the "implicit function theorem" [1]. It roughly states that multivalu...

A connectionist model made up of a combination of RBF networks is proposed; the model decomposes multivalued dependencies into local single valued functions; theory and applications are presented. 1 Introduction We present a new network structure which is modelled according to the "implicit function theorem" [1]. It roughly states that multivalued functions and relations, which can be described by the zeros of an implicit function, can locally be represented by single valued functions. In this network, the local functions are realized by feedforward networks (RBF). They are incorporated into a global network by a symmetric topological encoding of the in- and output spaces and by a product of error functions. The latter represent separated classes of local functions. Via a least square training of the global network it is decided which one of the local networks generalizes best in a special region. This optimal network is then used for the local generalization of the multivalued fun. Minimize

Publisher:

University of Skovde, Springer Verlag

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-14

Source:

ftp://ftp.stat.uni-muenchen.de/pub/sfb386/paper116.ps.Z

ftp://ftp.stat.uni-muenchen.de/pub/sfb386/paper116.ps.Z Minimize

Document Type:

text

Language:

en

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

Metadata may be used without restrictions as long as the oai identifier remains attached to it. Minimize

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Title:

SPATIO-TEMPORAL DATA ANALYSIS WITH NON-LINEAR FILTERS: BRAIN MAPPING WITH fMRI DATA

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Spatio-temporal digital data from fMRI (functional Magnetic Resonance Imaging) are used to analyse and to model brain activation. To map brain functions, a well-defined sensory activation is offered to a test person and the hemodynamic response to neuronal activity is studied. This so-called BOLD effect in fMRI is typically small and characteris...

Spatio-temporal digital data from fMRI (functional Magnetic Resonance Imaging) are used to analyse and to model brain activation. To map brain functions, a well-defined sensory activation is offered to a test person and the hemodynamic response to neuronal activity is studied. This so-called BOLD effect in fMRI is typically small and characterised by a very low signal to noise ratio. Hence the activation is repeated and the three dimensional signal (multi-slice 2D) is gathered during relatively long time ranges (3-5 min). From the noisy and distorted spatio-temporal signal the expected response has to be filtered out. Presented methods of spatio-temporal signal processing base on non-linear concepts of data reconstruction and filters of mathematical morphology (e.g. alternating sequential morphological filters). Filters applied are compared by classifications of activations. Minimize

Publisher:

Slovenian Society for Stereology and Quantitative Image Analysis

Year of Publication:

2011-05-01T00:00:00Z

Document Type:

article

Language:

English

Subjects:

brain mapping ; functional magnetic resonance imaging ; non-linear filtering ; mathematical morphology ; spatio-temporal image analysis ; LCC:Medicine (General) ; LCC:R5-920 ; LCC:Medicine ; LCC:R ; DOAJ:Medicine (General) ; DOAJ:Health Sciences ; LCC:Mathematics ; LCC:QA1-939 ; LCC:Science ; LCC:Q ; DOAJ:Mathematics ; DOAJ:Mathematics and Stati...

brain mapping ; functional magnetic resonance imaging ; non-linear filtering ; mathematical morphology ; spatio-temporal image analysis ; LCC:Medicine (General) ; LCC:R5-920 ; LCC:Medicine ; LCC:R ; DOAJ:Medicine (General) ; DOAJ:Health Sciences ; LCC:Mathematics ; LCC:QA1-939 ; LCC:Science ; LCC:Q ; DOAJ:Mathematics ; DOAJ:Mathematics and Statistics ; LCC:Medicine (General) ; LCC:R5-920 ; LCC:Medicine ; LCC:R ; LCC:Mathematics ; LCC:QA1-939 ; LCC:Science ; LCC:Q Minimize

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CC by-nc

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http://www.ias-iss.org/ojs/IAS/article/view/645

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Title:

A Brief Survey of Recent Edge-Preserving Smoothers

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Description:

. We introduce recent and very recent smoothing methods and discuss them in the common framework of `energy functions'. Focus is on the preservation of boundaries, spikes and canyons in presence of noise. 1 Introduction There is rapidly increasing interest in models and methods for discontinuous phenomena, both in the mathematical and statistica...

. We introduce recent and very recent smoothing methods and discuss them in the common framework of `energy functions'. Focus is on the preservation of boundaries, spikes and canyons in presence of noise. 1 Introduction There is rapidly increasing interest in models and methods for discontinuous phenomena, both in the mathematical and statistical community. The focus is on the identification of discontinuities in data perturbed by noise. This is of particular importance in imaging, where noise has to be removed from image data while preserving relevant basic features like jumps, spikes and boundaries. Plainly, methods dealing for instance with boundary extraction have a long tradition in imaging and are an own `applied art'. Nevertheless, recent contributions from mathematics and statistics promise new approaches and/or a deeper analysis of statistical properties and performance. With this slender paper we try to contribute to the communication between the imaging and the statistical . Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-12

Source:

ftp://ftp.stat.uni-muenchen.de/pub/sfb386/paper132.ps.Z

ftp://ftp.stat.uni-muenchen.de/pub/sfb386/paper132.ps.Z Minimize

Document Type:

text

Language:

en

Subjects:

Edge-Preserving Smoothing ; Noise Reduction ; Edge Detection ; Nonlinear Filtering

Edge-Preserving Smoothing ; Noise Reduction ; Edge Detection ; Nonlinear Filtering Minimize

DDC:

310 Collections of general statistics *(computed)*

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

Metadata may be used without restrictions as long as the oai identifier remains attached to it. Minimize

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Title:

Intensity Segmentation of the Human Brain with Tissue dependent Homogenization

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Description:

High-precision segmentation of the human cerebral cortex based on T1-weighted MRI is still a challenging task. When opting to use an intensity based approach, careful data processing is mandatory to overcome inaccuracies. They are caused by noise, partial volume effects and systematic signal intensity variations imposed by limited homogeneity of...

High-precision segmentation of the human cerebral cortex based on T1-weighted MRI is still a challenging task. When opting to use an intensity based approach, careful data processing is mandatory to overcome inaccuracies. They are caused by noise, partial volume effects and systematic signal intensity variations imposed by limited homogeneity of the acquisition hardware. We propose an intensity segmentation which is free from any shape prior. It uses for the first time alternatively grey (GM) or white matter (WM) based homogenization. This new tissue dependency was introduced as the analysis of 60 high resolution MRI datasets revealed appreciable differences in the axial bias field corrections, depending if they are based on GM or WM. Homogenization starts with axial bias correction, a spatially irregular distortion correction follows and finally a noise reduction is applied. The construction of the axial bias correction is based on partitions of a depth histogram. The irregular bias is modelled by Moody Darken radial basis functions. Noise is eliminated by nonlinear edge preserving and homogenizing filters. A critical point is the estimation of the training set for the irregular bias correction in the GM approach. Because of intensity edges between CSF (cerebro spinal fluid surrounding the brain and within the ventricles), GM and WM this estimate shows an acceptable stability. By this supervised approach a high flexibility and precision for the segmentation of normal and pathologic brains is gained. The precision of this approach is shown using the Montreal brain phantom. Real data applications exemplify the advantage of the GM based approach, compared to the usual WM homogenization, allowing improved cortex segmentation. Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-09-15

Source:

http://epub.ub.uni-muenchen.de/1674/1/paper_296.pdf

http://epub.ub.uni-muenchen.de/1674/1/paper_296.pdf Minimize

Document Type:

text

Language:

en

Subjects:

⋆ This work was supported by DFG SFB 386

⋆ This work was supported by DFG SFB 386 Minimize

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

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Title:

Matrix Valency Regulates Integrin-mediated Lymphoid Adhesion via Syk Kinase

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Description:

Abstract. Lymphocytes accumulate within the extracellular matrix (ECM) of tumor, wound, or inflammatory tissues. These tissues are largely comprised of polymerized adhesion proteins such as fibrin and fibronectin or their fragments. Nonactivated lymphoid cells attach preferentially to polymerized ECM proteins yet are unable to attach to monomeri...

Abstract. Lymphocytes accumulate within the extracellular matrix (ECM) of tumor, wound, or inflammatory tissues. These tissues are largely comprised of polymerized adhesion proteins such as fibrin and fibronectin or their fragments. Nonactivated lymphoid cells attach preferentially to polymerized ECM proteins yet are unable to attach to monomeric forms or fragments of these proteins without previous activation. This adhesion event depends on the appropriate spacing of integrin adhesion sites. Adhesion of nonactivated lymphoid cells to polymeric ECM components results in activation of the antigen receptor-associated Syk kinase that accumulates in adhesion-promoting podosomes. In fact, activation of Syk by antigen or agonists, as well as expression of an activated Syk mutant in lymphoid Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2013-03-28

Source:

ftp://ftp.ncbi.nlm.nih.gov/pub/pmc/20/e9/J_Cell_Biol_1999_Feb_22_144(4)_777-788.tar.gz

ftp://ftp.ncbi.nlm.nih.gov/pub/pmc/20/e9/J_Cell_Biol_1999_Feb_22_144(4)_777-788.tar.gz Minimize

Document Type:

text

Language:

en

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Title:

Spatio-Temporal Data Analysis with Non-Linear Filters: Brain Mapping with fMRI Data

Author:

Description:

Spatio-temporal digital data from fMRI (functional Magnetic Resonance Imaging) are used to analyse and to model brain activation. To map brain functions, a well-defined sensory activation is offered to a test person and the hemodynamic response to neuronal activity is studied. This so-called BOLD effect in fMRI is typically small and characteris...

Spatio-temporal digital data from fMRI (functional Magnetic Resonance Imaging) are used to analyse and to model brain activation. To map brain functions, a well-defined sensory activation is offered to a test person and the hemodynamic response to neuronal activity is studied. This so-called BOLD effect in fMRI is typically small and characterised by a very low signal to noise ratio. Hence the activation is repeated and the three dimensional signal (multi-slice 2D) is gathered during relatively long time ranges (3-5 min). From the noisy and distorted spatio-temporal signal the expected response has to be filtered out. Presented methods of spatio-temporal signal processing base on non-linear concepts of data reconstruction and filters of mathematical morphology (e.g. alternating sequential morphological filters). Filters applied are compared by classifications of activations. Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-16

Source:

http://www.gsf.de/ILIAD/persons/Rodenacker/Misc_WWW/pdf/SCIAMAL99.pdf

http://www.gsf.de/ILIAD/persons/Rodenacker/Misc_WWW/pdf/SCIAMAL99.pdf Minimize

Document Type:

text

Language:

en

Subjects:

brain mapping ; functional magnetic resonance imaging ; non-linear filtering ; mathematical morphology ; spatio-temporal

brain mapping ; functional magnetic resonance imaging ; non-linear filtering ; mathematical morphology ; spatio-temporal Minimize

DDC:

621 Applied physics *(computed)*

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Title:

Intensity Segmentation of the Human Brain with Tissue dependent Homogenization ⋆

Author:

Description:

High-precision segmentation of the human cerebral cortex based on T1-weighted MRI is still a challenging task. When opting to use an intensity based approach, careful data processing is mandatory to overcome inaccuracies. They are caused by noise, partial volume effects and systematic signal intensity variations imposed by limited homogeneity of...

High-precision segmentation of the human cerebral cortex based on T1-weighted MRI is still a challenging task. When opting to use an intensity based approach, careful data processing is mandatory to overcome inaccuracies. They are caused by noise, partial volume effects and systematic signal intensity variations imposed by limited homogeneity of the acquisition hardware. We propose an intensity segmentation which is free from any shape prior. It uses for the first time alternatively grey (GM) or white matter (WM) based homogenization. This new tissue dependency was introduced as the analysis of 60 high resolution MRI datasets revealed appreciable differences in the axial bias field corrections, depending if they are based on GM or WM. Homogenization starts with axial bias correction, a spatially irregular distortion correction follows and finally a noise reduction is applied. The construction of the axial bias correction is based on partitions of a depth histogram. The irregular bias is modelled by Moody Darken radial basis functions. Noise is eliminated by nonlinear edge preserving and homogenizing filters. A critical point is the estimation of the training set for the irregular bias correction in the GM approach. Because of intensity edges between CSF (cerebro spinal fluid surrounding the brain and within the ventricles), GM and WM this estimate shows an acceptable stability. By this supervised approach a high flexibility and precision for the segmentation of normal and pathologic brains is gained. The precision of this approach is shown using the Montreal brain phantom. Real data applications exemplify the advantage of the GM based approach, compared to the usual WM homogenization, allowing improved cortex segmentation. Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2008-07-01

Source:

http://ibb.gsf.de/preprints/2002/pp02-21.pdf

http://ibb.gsf.de/preprints/2002/pp02-21.pdf Minimize

Document Type:

text

Language:

en

Subjects:

⋆ This work was supported by DFG SFB 386

⋆ This work was supported by DFG SFB 386 Minimize

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Title:

Geophysics, Siberian Branch of Russian Academy of Sciences ‡

Author:

Description:

of two numerical methods to

of two numerical methods to Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2013-08-12

Source:

http://www.helmholtz-muenchen.de/fileadmin/IBB/PDF/Research/Preprints/Preprints_2007/pp07-01.pdf

http://www.helmholtz-muenchen.de/fileadmin/IBB/PDF/Research/Preprints/Preprints_2007/pp07-01.pdf Minimize

Document Type:

text

Language:

en

Subjects:

fractional Brownian motion

fractional Brownian motion Minimize

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Title:

Recent Developments in Edge-Preserving Smoothing

Author:

Description:

Abstract. We introduce recent and very recent smoothing methods and discuss them in the common framework of `energy functions'. Focus is on the preservation of boundaries, spikes and canyons in presence of noise. 1 Introduction There is rapidly increasing interest in models and methods for discontinuous phenomena, both in the mathematical and st...

Abstract. We introduce recent and very recent smoothing methods and discuss them in the common framework of `energy functions'. Focus is on the preservation of boundaries, spikes and canyons in presence of noise. 1 Introduction There is rapidly increasing interest in models and methods for discontinuous phenomena, both in the mathematical and statistical community. The focus is on the identification of discontinuities in data perturbed by noise. This is of particular importance in imaging, where noise has to be removed from image data while preserving relevant basic features like jumps, spikes and boundaries. Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2008-07-01

Source:

http://ibb.gsf.de/preprints/1998/pp98-13.ps

http://ibb.gsf.de/preprints/1998/pp98-13.ps Minimize

Document Type:

text

Language:

en

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Title:

Spatial smoothing for Diffusion Tensor Imaging with low Signal to Noise Ratios

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Abstract: Though low signal to noise ratio (SNR) experiments in DTI give key information about tracking and anisotropy, e.g. by measurements with very small voxel sizes, due to the complicated impact of thermal noise such experiments are up to now seldom analysed. In this paper Monte Carlo simulations are presented which investigate the random f...

Abstract: Though low signal to noise ratio (SNR) experiments in DTI give key information about tracking and anisotropy, e.g. by measurements with very small voxel sizes, due to the complicated impact of thermal noise such experiments are up to now seldom analysed. In this paper Monte Carlo simulations are presented which investigate the random fields of noise for different DTI variables in low SNR situations. Based on this study a strategy for spatial smoothing, which demands essentially uniform noise, is derived. To construct a convenient filter the weights of the nonlinear Aurich chain are adapted to DTI. This edge preserving three dimensional filter is then validated in different variants via a quasi realistic model and is applied to very new data with isotropic voxels of the size 1x1x1 mm 3 which correspond to a spatial mean SNR of approximately 3. Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2008-07-01

Source:

http://epub.ub.uni-muenchen.de/1733/1/paper_358.pdf

http://epub.ub.uni-muenchen.de/1733/1/paper_358.pdf Minimize

Document Type:

text

Language:

en

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