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TUMOR SEGMENTATION FROM A MULTISPECTRAL MRI IMAGES BY USING SUPPORT VECTOR MACHINE CLASSIFICATION | IEEE Conference Publication | IEEE Xplore

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


Abstract:

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 M...Show More

Abstract:

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.
Date of Conference: 12-15 April 2007
Date Added to IEEE Xplore: 15 May 2007
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Conference Location: Arlington, VA, USA

1. INTRODUCTION

Magnetic resonance imaging (MRI) is a highly successful diagnostic imaging modality, largely due to its ability to derive contrast from a number of physical parameters. The different types of MR images obtained from the different of excitation sequences, also called multispectral images, can provide different image intensity information for a given anatomical region and subject. As a tumor consists of different biologic tissues, one type of MRI cannot give complete information about abnormal tissues. Therefore, radiology experts always combine the multispectral MRI volumes of one patient to take a decision on the location, extension, prognosis and diagnosis of the tumors. That is why it is necessary to fuse multispectral MRI information to segment tumor regions.

References

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