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

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

WoS Article

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.

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.

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

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

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

2013

31.07.2012

De Gruyter Open

1335-8871

Measurement Science Review

12

4

Slovak Republic

153

161

9

https://www.degruyter.com/view/j/msr.2012.12.issue-4/v10048-012-0023-8/v10048-012-0023-8.xml

http://hdl.handle.net/11012/68378

msr-2012