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Statements

Subject Item
n2:RIV%2F60461373%3A22340%2F09%3A00022069%21RIV10-MSM-22340___
rdf:type
skos:Concept n14:Vysledek
dcterms:description
High-grade gliomas represent rapidly growing malignant brain tumours. Early diagnostics of this decease and immediately applied treatment entails better life prognosis for the patient. The goal of this work is to initialize the development of an automated tumour recognition method based on computed tomography images processing. The resulting method is aimed at early glioma diagnostics support by distinguishing between the healthy tissue and the tumour tissue. The proposed technique involves, subsequently, image preprocessing, feature extraction, and classification of the extracted features using an artificial neural network. To obtain features, we compute the skewness of the discrete wavelet transform coefficients from selected rectangular image regions using three neighboring image slices to increase the number of samples while preserving good segmentation resolution within the image. The segmentation results are evaluated in cooperation with the neurologist. High-grade gliomas represent rapidly growing malignant brain tumours. Early diagnostics of this decease and immediately applied treatment entails better life prognosis for the patient. The goal of this work is to initialize the development of an automated tumour recognition method based on computed tomography images processing. The resulting method is aimed at early glioma diagnostics support by distinguishing between the healthy tissue and the tumour tissue. The proposed technique involves, subsequently, image preprocessing, feature extraction, and classification of the extracted features using an artificial neural network. To obtain features, we compute the skewness of the discrete wavelet transform coefficients from selected rectangular image regions using three neighboring image slices to increase the number of samples while preserving good segmentation resolution within the image. The segmentation results are evaluated in cooperation with the neurologist.
dcterms:title
Brain Tumour Diagnostics Support Based on Medical Image Segmentation Brain Tumour Diagnostics Support Based on Medical Image Segmentation
skos:prefLabel
Brain Tumour Diagnostics Support Based on Medical Image Segmentation Brain Tumour Diagnostics Support Based on Medical Image Segmentation
skos:notation
RIV/60461373:22340/09:00022069!RIV10-MSM-22340___
n3:aktivita
n17:Z
n3:aktivity
Z(MSM6046137306)
n3:dodaniDat
n15:2010
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n6:D
n3:duvernostUdaju
n16:S
n3:entitaPredkladatele
n20:predkladatel
n3:idSjednocenehoVysledku
305479
n3:idVysledku
RIV/60461373:22340/09:00022069
n3:jazykVysledku
n21:eng
n3:klicovaSlova
high-grade glioma; brain tumour detection; skewness; wavelet transform; feature extraction and classification
n3:klicoveSlovo
n7:wavelet%20transform n7:feature%20extraction%20and%20classification n7:high-grade%20glioma n7:skewness n7:brain%20tumour%20detection
n3:kontrolniKodProRIV
[155C7FE95298]
n3:mistoKonaniAkce
Praha
n3:mistoVydani
Praha
n3:nazevZdroje
Proceedings 17th Annual Conference Technical Computing Prague 2009
n3:obor
n11:JD
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
3
n3:rokUplatneniVysledku
n15:2009
n3:tvurceVysledku
Hošťálková, Eva Měřínský, Zdeněk Procházka, Aleš
n3:typAkce
n18:EUR
n3:zahajeniAkce
2009-11-19+01:00
n3:zamer
n8:MSM6046137306
s:numberOfPages
7
n12:hasPublisher
Humusoft
n13:isbn
978-80-7080-733-0
n10:organizacniJednotka
22340