<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>The paper deals with modern methods of image processing, especially image segmentation, classification and evaluation of parameters. It focuses 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 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 focuses 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 which demonstrate the very good properties of the active contour method are given.</p></abstract>

The paper deals with modern methods of image processing, especially image segmentation, classification and evaluation of parameters. It focuses 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 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 focuses 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 which demonstrate the very good properties of the active contour method are given.

Soft-tissues Image Processing: Comparison of Traditional Segmentation Methods with 2D active Contour Methods

Department of Theoretical and Experimental Electrical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Kolejni 2906/4, 612 00, Brno, Czech Republic

Institute of Scientific Instruments, Academy of Science, Kralovopolska 147, 612 64, Brno, Czech Republic

Measurement Science Review

The paper deals with modern methods of image processing, especially image segmentation, classification and evaluation of parameters. It focuses primarily on...

Soft-tissues Image Processing: Comparison of Traditional Segmentation Methods with 2D active Contour Methods

Data publikacji: 13 sie 2012

Zakres stron: 153 - 161

DOI: https://doi.org/10.2478/v10048-012-0023-8

Słowa kluczowe
Medical image processing, image segmentation, liver tumor, temporomandibular joint disc, watershed method

This content is open access.

Soft-tissues Image Processing: Comparison of Traditional Segmentation Methods with 2D active Contour Methods

Data publikacji: 13 sie 2012

Zakres stron: 153 - 161

DOI: https://doi.org/10.2478/v10048-012-0023-8

Słowa kluczoweMedical image processing, image segmentation, liver tumor, temporomandibular joint disc, watershed method

This content is open access.

Słowa kluczowe
Medical image processing, image segmentation, liver tumor, temporomandibular joint disc, watershed method