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Statements

Subject Item
n2:RIV%2F68081731%3A_____%2F12%3A00385188%21RIV13-GA0-68081731
rdf:type
skos:Concept n18:Vysledek
dcterms:description
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. 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.
dcterms:title
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
skos:prefLabel
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
skos:notation
RIV/68081731:_____/12:00385188!RIV13-GA0-68081731
n18:predkladatel
n19:ico%3A68081731
n3:aktivita
n12:I n12:S n12:P
n3:aktivity
I, P(ED0017/01/01), P(GAP102/11/0318), P(GAP102/12/1104), S
n3:cisloPeriodika
4
n3:dodaniDat
n7:2013
n3:domaciTvurceVysledku
n16:4095499
n3:druhVysledku
n14:J
n3:duvernostUdaju
n15:S
n3:entitaPredkladatele
n11:predkladatel
n3:idSjednocenehoVysledku
168908
n3:idVysledku
RIV/68081731:_____/12:00385188
n3:jazykVysledku
n8:eng
n3:klicovaSlova
Medical image processing; image segmentation; liver tumor; temporomandibular joint disc; watershed method
n3:klicoveSlovo
n5:temporomandibular%20joint%20disc n5:Medical%20image%20processing n5:watershed%20method n5:image%20segmentation n5:liver%20tumor
n3:kodStatuVydavatele
SK - Slovenská republika
n3:kontrolniKodProRIV
[99CBE58DDCCE]
n3:nazevZdroje
Measurement Science Review
n3:obor
n17:JA
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
3
n3:projekt
n6:GAP102%2F12%2F1104 n6:GAP102%2F11%2F0318 n6:ED0017%2F01%2F01
n3:rokUplatneniVysledku
n7:2012
n3:svazekPeriodika
12
n3:tvurceVysledku
Mikulka, J. Bartušek, Karel Gescheidtová, E.
n3:wos
000307943000006
s:issn
1335-8871
s:numberOfPages
9
n10:doi
10.2478/v10048-012-0023-8