Multiparametric Biological Tissue Analysis: A Survey of Image Processing Tools

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Multiparametric Biological Tissue Analysis: A Survey of Image Processing Tools

Popis výsledku v původním jazyce

Using magnetic resonance tomography to scan biological tissues is currently a very dynamic approach. Based on various image parameters, the method enables us to analyze tissue properties, recognize healthy and pathological tissues, and diagnose the disease or indicate its progression. These activities are then necessarily accompanied by the processing of the acquired images. The paper introduces a comparison of statistical tools for the trainable segmentation of multiparametric data obtained through magnetic resonance tomography. In this context, the author briefly compares various available tools (Weka, Slicer3D, and RapidMiner) in view of the input data training and testing, applicability of the classification models, and ability of the input/output data to be extended with other systems for further processing. The paper also describes as a multiparametric task the segmentation of a brain tumor performed with real MR data. The source of the data consists in T1 and T2-weighted images. The proposed segmentation method is carried out within the following phases: data resampling; spatial data coregistration; definition of the training points; training of the SVM classification model; testing of the model and interpretation of the classification results.

Název v anglickém jazyce

Multiparametric Biological Tissue Analysis: A Survey of Image Processing Tools

Popis výsledku anglicky

Using magnetic resonance tomography to scan biological tissues is currently a very dynamic approach. Based on various image parameters, the method enables us to analyze tissue properties, recognize healthy and pathological tissues, and diagnose the disease or indicate its progression. These activities are then necessarily accompanied by the processing of the acquired images. The paper introduces a comparison of statistical tools for the trainable segmentation of multiparametric data obtained through magnetic resonance tomography. In this context, the author briefly compares various available tools (Weka, Slicer3D, and RapidMiner) in view of the input data training and testing, applicability of the classification models, and ability of the input/output data to be extended with other systems for further processing. The paper also describes as a multiparametric task the segmentation of a brain tumor performed with real MR data. The source of the data consists in T1 and T2-weighted images. The proposed segmentation method is carried out within the following phases: data resampling; spatial data coregistration; definition of the training points; training of the SVM classification model; testing of the model and interpretation of the classification results.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of PIERS 2014 in Guangzhou

ISBN

978-1-934142-28-8

ISSN

1559-9450

e-ISSN

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Počet stran výsledku

4

Strana od-do

1861-1864

Název nakladatele

Neuveden

Místo vydání

Guangzhou, Čína

Místo konání akce

Guangzhou

Datum konání akce

25. 8. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000393225900412