About: Automatic Segmentation of Multi-Contrast MRI Using Statistical Region Merging

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An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
Description	Several methods have been developed for segmentation of MR images. Some of them are fully automated and some of them rely on an expert's assistance, such as determination of a starting point etc. The fully automated methods are usually based on prior knowledge of a given object and can be used only for particular problem. The purpose of the proposed method is a fully automatic segmentation for general MR images independent on the number of tissues present. The proposed method is based on Statistical Region Merging (SRM) algorithm developed by Richard Nock and Frank Nielsen in 2004. The suitable MR contrasts for this algorithm, as it was confirmed during the test phase, are T1, T2 and FLAIR images. The segmentation process divides to image into regions according the properties in the area, but it does not consider the unconnected areas. For this reason, the algorithm is repeated for created segments without a joint border condition. The algorithm was tested on 5000 axial images with resolution 256x256 Several methods have been developed for segmentation of MR images. Some of them are fully automated and some of them rely on an expert's assistance, such as determination of a starting point etc. The fully automated methods are usually based on prior knowledge of a given object and can be used only for particular problem. The purpose of the proposed method is a fully automatic segmentation for general MR images independent on the number of tissues present. The proposed method is based on Statistical Region Merging (SRM) algorithm developed by Richard Nock and Frank Nielsen in 2004. The suitable MR contrasts for this algorithm, as it was confirmed during the test phase, are T1, T2 and FLAIR images. The segmentation process divides to image into regions according the properties in the area, but it does not consider the unconnected areas. For this reason, the algorithm is repeated for created segments without a joint border condition. The algorithm was tested on 5000 axial images with resolution 256x256 (en)
Title	Automatic Segmentation of Multi-Contrast MRI Using Statistical Region Merging Automatic Segmentation of Multi-Contrast MRI Using Statistical Region Merging (en)
skos:prefLabel	Automatic Segmentation of Multi-Contrast MRI Using Statistical Region Merging Automatic Segmentation of Multi-Contrast MRI Using Statistical Region Merging (en)
skos:notation	RIV/00216305:26220/14:PU110262!RIV15-MSM-26220___
http://linked.open...avai/riv/aktivita	I P S
http://linked.open...avai/riv/aktivity	I, P(GAP102/12/1104), P(LD14091), S
http://linked.open...vai/riv/dodaniDat	2015
http://linked.open...aciTvurceVysledku	Gescheidtová, Eva Dvořák, Pavel
http://linked.open.../riv/druhVysledku	D - Článek ve sborníku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Vysoké učení technické v Brně / Fakulta elektrotechniky a komunikačních technologií
http://linked.open...dnocenehoVysledku	4610
http://linked.open...ai/riv/idVysledku	RIV/00216305:26220/14:PU110262
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	Image Segmentation, MRI, Statistical Region Merging (en)
http://linked.open.../riv/klicoveSlovo	MRI Image Segmentation Statistical Region Merging
http://linked.open...ontrolniKodProRIV	[5B433FD85DD2]
http://linked.open...v/mistoKonaniAkce	Guangzhou
http://linked.open...i/riv/mistoVydani	Guangzhou
http://linked.open...i/riv/nazevZdroje	PIERS 2014 Guangzhou Proceedings
http://linked.open...in/vavai/riv/obor	IN
http://linked.open...ichTvurcuVysledku	2 (xsd:int)
http://linked.open...cetTvurcuVysledku	3 (xsd:int)
http://linked.open...vavai/riv/projekt	Primary and soft biometric and non-biometric traits in the de-identification process of audio-visual content in multimedia data Study of metabolism and localization of high grade glioma using MR imaging techniques
http://linked.open...UplatneniVysledku	2014
http://linked.open...iv/tvurceVysledku	Bartušek, Karel Dvořák, Pavel Gescheidtová, Eva
http://linked.open...vavai/riv/typAkce	WRD - Světová
http://linked.open.../riv/zahajeniAkce	2014-08-25 (xsd:date)
number of pages	5 (xsd:int)
http://purl.org/ne...btex#hasPublisher	Neuveden
https://schema.org/isbn	978-1-934142-28-8
http://localhost/t...ganizacniJednotka	26220

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