Publication detail

Soft-tissues image processing: comparison of traditional segmentation methods with 2D active contour methods

MIKULKA, J. GESCHEIDTOVÁ, E. BARTUŠEK, K.

Original Title

Soft-tissues image processing: comparison of traditional segmentation methods with 2D active contour methods

English Title

Soft-tissues image processing: comparison of traditional segmentation methods with 2D active contour methods

Type

journal article

Language

en

Original Abstract

The paper deals with modern methods of image processing, especially image segmentation, classification and evaluation of parameters. It is focused primarily on processing medical images of soft tissues obtained by magnetic resonance tomography (MR). It is easy to describe edges of the sought objects using of segmented images. The edges found can be useful for further processing of monitored object such as calculating the perimeter, surface and volume evaluation or even three-dimensional shape reconstruction. The proposed solutions can be used for the classification of healthy/unhealthy tissues in MR or other imaging. Application examples of the proposed segmentation methods are shown. Research in the area of image segmentation is focused on methods based on solving partial differential equations. This is a modern method for image processing, often called the active contour method. It is of great advantage in the segmentation of real images degraded by noise with fuzzy edges and transitions between objects. In the paper, results of the segmentation of medical images by the active contour method are compared with results of the segmentation by other existing methods. Experimental applications are given which demonstrate the very good properties of the active contour method.

English abstract

The paper deals with modern methods of image processing, especially image segmentation, classification and evaluation of parameters. It is focused primarily on processing medical images of soft tissues obtained by magnetic resonance tomography (MR). It is easy to describe edges of the sought objects using of segmented images. The edges found can be useful for further processing of monitored object such as calculating the perimeter, surface and volume evaluation or even three-dimensional shape reconstruction. The proposed solutions can be used for the classification of healthy/unhealthy tissues in MR or other imaging. Application examples of the proposed segmentation methods are shown. Research in the area of image segmentation is focused on methods based on solving partial differential equations. This is a modern method for image processing, often called the active contour method. It is of great advantage in the segmentation of real images degraded by noise with fuzzy edges and transitions between objects. In the paper, results of the segmentation of medical images by the active contour method are compared with results of the segmentation by other existing methods. Experimental applications are given which demonstrate the very good properties of the active contour method.

Keywords

Medical image processing, image segmentation, liver tumour, temporomandibular joint disc, level set, active contours, thresholding, watershed, edge detectors

RIV year

2012

Released

31.07.2012

Publisher

De Gruyter Open

Pages from

153

Pages to

161

Pages count

9

URL

Full text in the Digital Library

BibTex


@article{BUT92915,
  author="Jan {Mikulka} and Eva {Gescheidtová} and Karel {Bartušek}",
  title="Soft-tissues image processing: comparison of traditional segmentation methods with 2D active contour methods",
  annote="The paper deals with modern methods of image processing, especially image segmentation, classification and evaluation of parameters. It is focused primarily on processing medical images of soft tissues obtained by magnetic resonance tomography (MR). It is easy to describe edges of the sought objects using of segmented images. The edges found can be useful for further processing of monitored object such as calculating the perimeter, surface and volume evaluation or even three-dimensional shape reconstruction. The proposed solutions can be used for the classification of healthy/unhealthy tissues in MR or other imaging. Application examples of the proposed segmentation methods are shown. Research in the area of image segmentation is focused on methods based on solving partial differential equations. This is a modern method for image processing, often called the active contour method. It is of great advantage in the segmentation of real images degraded by noise with fuzzy edges and transitions between objects. In the paper, results of the segmentation of medical images by the active contour method are compared with results of the segmentation by other existing methods. Experimental applications are given which demonstrate the very good properties of the active contour method.",
  address="De Gruyter Open",
  chapter="92915",
  doi="10.2478/v10048-012-0023-8",
  institution="De Gruyter Open",
  number="4",
  volume="12",
  year="2012",
  month="july",
  pages="153--161",
  publisher="De Gruyter Open",
  type="journal article"
}