Segmentation de tumeur cérébrale à partir d'IRM multi-séquences par apprentissage de noyaux multiples
Identifieur interne : 000403 ( Main/Exploration ); précédent : 000402; suivant : 000404Segmentation de tumeur cérébrale à partir d'IRM multi-séquences par apprentissage de noyaux multiples
Auteurs : Naouel Boughattas [France] ; Maxime Berar [France] ; Kamel Hamrouni [Tunisie] ; Su Ruan [France]Source :
English descriptors
Abstract
Doctors use several types of MRI images (Magnetic resonance imaging) to diagnose brain tumours and estimate their volumes. To bring a help to the doctors, we treat here the fusion of the various information sources to segment the tumor. We propose a segmentation method based on the classication allowing to take into account the various features of each type of image. This method is based on a multiple kernel learning SVM algorithm which selects the most relevant features by associating a kernel for each extracted feature. The weight of the kernel reects the relevance of the corresponding feature. The feature selection and the selection of the regularization parameter of the SVM are made by a fully automatic way. The method has been tested on 7 real patients. We show in this article a comparison with another recent method which was compared with various methods to show its superiority [ 2 ]. Our study of comparison shows a better performance of our method.
Url:
Affiliations:
- France, Tunisie
- Haute-Normandie, Région Normandie
- Le Havre, Rouen
- Université de Rouen, Université du Havre
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Le document en format XML
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<front><div type="abstract" xml:lang="en">Doctors use several types of MRI images (Magnetic resonance imaging) to diagnose brain tumours and estimate their volumes. To bring a help to the doctors, we treat here the fusion of the various information sources to segment the tumor. We propose a segmentation method based on the classication allowing to take into account the various features of each type of image. This method is based on a multiple kernel learning SVM algorithm which selects the most relevant features by associating a kernel for each extracted feature. The weight of the kernel reects the relevance of the corresponding feature. The feature selection and the selection of the regularization parameter of the SVM are made by a fully automatic way. The method has been tested on 7 real patients. We show in this article a comparison with another recent method which was compared with various methods to show its superiority [ 2 ]. Our study of comparison shows a better performance of our method.</div>
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<country name="Tunisie"><noRegion><name sortKey="Hamrouni, Kamel" sort="Hamrouni, Kamel" uniqKey="Hamrouni K" first="Kamel" last="Hamrouni">Kamel Hamrouni</name>
</noRegion>
</country>
</tree>
</affiliations>
</record>
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