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Statements

Subject Item
n2:RIV%2F68081731%3A_____%2F12%3A00386210%21RIV13-GA0-68081731
rdf:type
n4:Vysledek skos:Concept
dcterms:description
Image processing in biomedical applications is an important developing issue. Many methods and approaches for image preprocessing, segmentation and visualization were described. It is necessary to choose a suitable segmentation method to create a correct three-dimensional model. The accuracy of reconstruction depends on precision of regions boundary determining in magnetic resonance slices. A frequent application is detection of soft tissues. To obtain images of the soft tissues mentioned, tomography based on magnetic resonance is usually used. Ideally, several tissue slices in three orthogonal planes (sagittal, coronal, transverse) are acquired. Following reconstruction of shape of examined tissues is the most accurate. In case of acquired slices only in one plane, the high spatial information lost occurs by image acquisition. Then it is necessary to reconstruct the shape of tissue appropriately. At first the images are segmented and with use of particular segments the three dimensional model is composed. This article compares several segmentation approaches of magnetic resonance images and their results. The results of segmentation by active contour, thresholding, edge analysis by Sobel mask, watershed and region-based level set segmentation methods are compared. The results for different values of parameters of segmentation methods are compared. As the test image, slice of human liver tumour was chosen. Image processing in biomedical applications is an important developing issue. Many methods and approaches for image preprocessing, segmentation and visualization were described. It is necessary to choose a suitable segmentation method to create a correct three-dimensional model. The accuracy of reconstruction depends on precision of regions boundary determining in magnetic resonance slices. A frequent application is detection of soft tissues. To obtain images of the soft tissues mentioned, tomography based on magnetic resonance is usually used. Ideally, several tissue slices in three orthogonal planes (sagittal, coronal, transverse) are acquired. Following reconstruction of shape of examined tissues is the most accurate. In case of acquired slices only in one plane, the high spatial information lost occurs by image acquisition. Then it is necessary to reconstruct the shape of tissue appropriately. At first the images are segmented and with use of particular segments the three dimensional model is composed. This article compares several segmentation approaches of magnetic resonance images and their results. The results of segmentation by active contour, thresholding, edge analysis by Sobel mask, watershed and region-based level set segmentation methods are compared. The results for different values of parameters of segmentation methods are compared. As the test image, slice of human liver tumour was chosen.
dcterms:title
Magnetic Resonance Signal Processing in Medical Applications Magnetic Resonance Signal Processing in Medical Applications
skos:prefLabel
Magnetic Resonance Signal Processing in Medical Applications Magnetic Resonance Signal Processing in Medical Applications
skos:notation
RIV/68081731:_____/12:00386210!RIV13-GA0-68081731
n4:predkladatel
n15:ico%3A68081731
n3:aktivita
n10:I n10:S n10:P
n3:aktivity
I, P(ED0017/01/01), P(GAP102/11/0318), S
n3:dodaniDat
n5:2013
n3:domaciTvurceVysledku
n18:4095499
n3:druhVysledku
n7:D
n3:duvernostUdaju
n19:S
n3:entitaPredkladatele
n9:predkladatel
n3:idSjednocenehoVysledku
147782
n3:idVysledku
RIV/68081731:_____/12:00386210
n3:jazykVysledku
n8:eng
n3:klicovaSlova
magnetic resonance; biomedical image processing; image segmentation; level set; active countour; edge analysis; noise suppression; volumetry
n3:klicoveSlovo
n11:level%20set n11:volumetry n11:magnetic%20resonance n11:biomedical%20image%20processing n11:edge%20analysis n11:active%20countour n11:noise%20suppression n11:image%20segmentation
n3:kontrolniKodProRIV
[41E07C3B6C42]
n3:mistoKonaniAkce
Saint Gilles
n3:mistoVydani
Saint Gilles
n3:nazevZdroje
ICONS 2012: The Seventh Interatnional Conference on Systems
n3:obor
n13:JA
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
3
n3:projekt
n16:ED0017%2F01%2F01 n16:GAP102%2F11%2F0318
n3:rokUplatneniVysledku
n5:2012
n3:tvurceVysledku
Gescheidtová, E. Mikulka, J. Bartušek, Karel
n3:typAkce
n20:WRD
n3:zahajeniAkce
2012-02-29+01:00
s:numberOfPages
6
n6:hasPublisher
IARIA
n14:isbn
978-1-61208-184-7