Höherdimensionale Modelle zur Segmentierung biologischer Strukturen
type: CreativeWork
description: Many tasks in medical image processing require the robust segmentation of images. Information on the position and contour of objects allows the subsequent extraction of relevant quantitative information. This task is difficult due to actual imaging modalities that provide multi-dimensional (volumetric, time-variable, and multichannel) images. A newly formulated model is able to segment objects with arbitrary occurrence in images of any dimension. Model based segmentation methods are categorized. Subsequently, it is possible to formulate specifications that a model must meet for the robust segmentation of medical images. According to these specifications, a balloon-model is introduced. Objects are represented by a simplicial complex. Using mechanic simulations, this model is deformed to adapt to significant structures in an image. For the computation of image influences in single- and multichannel images, subsets of the same dimension as the image space itself are taken into account. The balloon-model is combined with a shape-based model. Shape knowledge from an automatically generated point distribution model is used to compute directed shape forces. The combination of all forces results in a segmentation result even if an initial contour is not given. The intersection of simplexes forms an inconsistency of the contour. This frequent problem for active contours is solved by methods that detect and correct such intersections. If necessary, these methods adaptively change the topology of objects. Further methods were developed to allow the transfer into clinical routine. The required parameter setting can be trained based on an exemplary segmentation. For heterogeneous image sets, more than one exemplary segmentation can be given. Then, an individual parameter set is computed for each image using global texture features and their similarity to prototype images. Non-contextual experiments on synthetic image material quantify the quality of segmentations for varying ima…
abstract: Many tasks in medical image processing require the robust segmentation of images. Information on the position and contour of objects allows the subsequent extraction of relevant quantitative information. This task is difficult due to actual imaging modalities that provide multi-dimensional (volumetric, time-variable, and multichannel) images. A newly formulated model is able to segment objects with arbitrary occurrence in images of any dimension. Model based segmentation methods are categorized. Subsequently, it is possible to formulate specifications that a model must meet for the robust segmentation of medical images. According to these specifications, a balloon-model is introduced. Objects are represented by a simplicial complex. Using mechanic simulations, this model is deformed to adapt to significant structures in an image. For the computation of image influences in single- and multichannel images, subsets of the same dimension as the image space itself are taken into account. The balloon-model is combined with a shape-based model. Shape knowledge from an automatically generated point distribution model is used to compute directed shape forces. The combination of all forces results in a segmentation result even if an initial contour is not given. The intersection of simplexes forms an inconsistency of the contour. This frequent problem for active contours is solved by methods that detect and correct such intersections. If necessary, these methods adaptively change the topology of objects. Further methods were developed to allow the transfer into clinical routine. The required parameter setting can be trained based on an exemplary segmentation. For heterogeneous image sets, more than one exemplary segmentation can be given. Then, an individual parameter set is computed for each image using global texture features and their similarity to prototype images. Non-contextual experiments on synthetic image material quantify the quality of segmentations for varying ima…
author: Bredno, Jörg
language: ger
keywords: info:eu-repo/classification/ddc/570, Biowissenschaften, Biologie
date published: 2002
date modified: 2026-07-30T19:42:01Z
provider: RWTH Aachen University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Publikationsserver der RWTH Aachen University