Hola,
¿eres nuevo aquí?

Regístrate gratis y conecta tu empresa con financiación pública, partners y proyectos.

Tengo cuenta

Regístrate

Ver video

GMM4MEDICAL

Financiado

Cerrado

Adaptive Gaussian Mixture Models for Continuous Representation of Digital Medica...

Adaptive Gaussian Mixture Models for Continuous Representation of Digital Medical Images In tomographic medical imaging, images are not acquired directly but sample data of statistical nature is measured from the patient placed in the field of view. From the acquired data, a volumetric image is then reconstructed by c... ver más

31/05/2012

RWTH AACHEN

30K€

Presupuesto del proyecto: 30K€

Líder del proyecto

RHEINISCHWESTFAELISCHE TECHNISCHE HOCHSCHULE... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo FP7 notifico la concesión del proyecto el día 2012-05-31 No tenemos la información de la convocatoria

0% 100% 100%

Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

1 Participantes

RWTH AACHEN

0.00€ | Lider

Proyectos interesantes

OpenGTN

Medical Technol...

Data Science

766K€ Cerrado

GlobalBioIm

Medical Technol...

Data Science

2M€ Cerrado

HD-Tomo

Medical Technol...

Data Science

2M€ Cerrado

PRE2018-086922

Medical Technol...

Data Science

93K€ Cerrado

FCCA

Medical Technol...

Data Science

2M€ Cerrado

PID2020-115225RB-I00

Medical Technol...

Data Science

41K€ Cerrado

Conecta tu I+D

¿Tienes un proyecto y buscas un partner? Gracias a nuestro motor inteligente podemos recomendarte los mejores socios y ponerte en contacto con ellos. Te lo explicamos en este video

Líder del proyecto

RHEINISCHWESTFAELISCHE TECHNISCHE HOCHSCHULE... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 30K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto In tomographic medical imaging, images are not acquired directly but sample data of statistical nature is measured from the patient placed in the field of view. From the acquired data, a volumetric image is then reconstructed by computational methods. Since the data acquisition pattern does not take into account the underlying image representation, reconstruction artifacts are likely to occur, especially when images are represented by uniform grids of voxels. As a consequence, images contain visible noise artifacts while the resolution is often insufficient in regions that would be supported by higher statistical information. Those regions are the focus of attention for image assessment and improving image resolution locally could provide a huge benefit for better detectability. Alternatives to the classical representation of images by grids of pixels and voxels exist but image modeling is not yet a very active field of research. Fortunately, the combination of modern developments in statistical estimation methods, approximation properties of polynomial B-spline basis functions and efficient hierarchical space partitioning data structures provide both theoretical justifications and efficient computational methods for the generation of high-quality adaptive image models from limited input data. The aim of this research project is to unveil a new way to represent continuous digital images in general. The paradigm of continuous image representation is totally new for medical imaging and contrasts with established discrete image models based on histograms. With such a sparse and continuous model, the image space is not limited by sharp boundaries and the number of image elements, hence the resolution, can be adapted locally as a function of the amount of input information available for image reconstruction. The techniques developed in this project will have a strong impact since they can be transferred to many other stochastic reconstruction scenarios.

Conecta tu I+D

Entra hoy

¿Olvidé mi contraseña?

Financiación

Empresas

CTIs/Universidades

Proyectos

Investigadores