TUMOR SEGMENTATION FROM A MULTISPECTRAL MRI IMAGES BY USING SUPPORT VECTOR MACHINE CLASSIFICATION

The goal of this paper is to present a supervised system aimed at tracking the tumor volume during a therapeutic treatment from multispectral MRI volumes. Four types of MRI are used in our study: T1, T2, proton density (PD) and fluid attenuated inversion recovery (FLAIR). For decreasing the processi...

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Bibliographic Details
Main Authors: Su Ruan, Lebonvallet, S., Merabet, A., Constans, J.-M.
Format: Conference Proceeding
Language:English
Subjects:
Biological tissues
Brain
Fuzzy logic
Image segmentation
Magnetic resonance imaging
Neoplasms
Protons
Robustness
Support vector machine classification
Support vector machines
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Summary:The goal of this paper is to present a supervised system aimed at tracking the tumor volume during a therapeutic treatment from multispectral MRI volumes. Four types of MRI are used in our study: T1, T2, proton density (PD) and fluid attenuated inversion recovery (FLAIR). For decreasing the processing time, the proposed method employs a multi-scale scheme to identify firstly the abnormal field and extract then the tumor region. Both steps use support vector machines (SVMs). The training is carried out only on the first MRI examination (at the beginning of the treatment). The tracking process at the time point t takes the tumor region obtained in the examination at t-1 as its initialization. Only the second step is performed for others examinations to extract the tumor region. The results obtained show that the proposed system achieves promising results in terms of effectiveness and time consuming.
ISSN:1945-7928
1945-8452
DOI:10.1109/ISBI.2007.357082