1) La descarga del recurso depende de la página de origen
2) Para poder descargar el recurso, es necesario ser usuario registrado en Universia


Opción 1: Descargar recurso

Detalles del recurso

Descripción

Prostate segmentation is essential for calculating prostate volume, creating patient-specific prostate anatomical models and image fusion. Automatic segmentation methods are preferable because manual segmentation is timeconsuming and highly subjective. Most of the currently available segmentation methods use a priori knowledge of the prostate shape. However, there is a large variation in prostate shape between patients. Our approach uses multispectral magnetic resonance imaging (MRI) data, containing T1, T2 and proton density (PD) weighted images and the distance from the voxel to the centroid of the prostate, together with statistical pattern classifiers. We investigated the performance of a parametric and a non-parametric classification approach by applying a Baysian-quadratic and a k-nearest-neighbor classifier respectively. An annotated data set is made by manual labeling of the image. Using this data set, the classifiers are trained and evaluated. sThe following results are obtained after three experiments. Firstly, using feature selection we showed that the average segmentation error rates are lowest when combining all three images and the distance with the k-nearest-neighbor classifier. Secondly, the confusion matrix showed that the k-nearest-neighbor classifier has the sensitivity. Finally, the prostate is segmented using both classifier. The segmentation boundaries approach the prostate boundaries for most slices. However, in some slices the segmentation result contained errors near the borders of the prostate. The current results showed that segmenting the prostate using multispectral MRI data combined with a statistical classifier is a promising method.

Pertenece a

University of Twente Publications  

Autor(es)

Maan, Bianca -  Heijden van der, Ferdi -  Fütterer, Jurgen J. - 

Id.: 55238660

Versión: 1.0

Estado: Final

Tipo:  application/pdf - 

Tipo de recurso: Article in monograph or in proceedings  - 

Tipo de Interactividad: Expositivo

Nivel de Interactividad: muy bajo

Audiencia: Estudiante  -  Profesor  -  Autor  - 

Estructura: Atomic

Coste: no

Copyright: sí

: © 2012 SPIE

Formatos:  application/pdf - 

Requerimientos técnicos:  Browser: Any - 

Relación: [References] http://doc.utwente.nl/80234/1/SPIE_article.pdf

Fecha de contribución: 04-may-2012

Contacto:

Localización:

Otros recursos de la misma colección

  1. Presenting a Framework to Analyze Local Climate Policy and Action in Small and Medium-Sized Cities Academic attention to local climate policy usually focuses on large-sized cities. Given the climate ...
  2. Autonomous Planning and Control of Soft Untethered Grippers in Unstructured Environments The use of small, maneuverable, untethered and reconfigurable robots could provide numerous advantag...
  3. Use of Gleeble MAXStrain unit for study of damage development in hot forging The standard Gleeble MAXStrain unit has been modified to allow axial elongation. Analyses indicate t...
  4. Symbiotic sensing: exploring and exploiting cooperative sensing in heterogeneous sensor networks In a few years’ time sensing with mobile devices like smartphones will pervade our daily life includ...
  5. Model checking and evaluating QoS of batteries in MPSoC dataflow applications via hybrid automata System lifetime is a major design constraint for battery-powered mobile embedded systems. The increa...

Aviso de cookies: Usamos cookies propias y de terceros para mejorar nuestros servicios, para análisis estadístico y para mostrarle publicidad. Si continua navegando consideramos que acepta su uso en los términos establecidos en la Política de cookies.