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Pattern Analysis and Applications
Erschienen in: Pattern Analysis and Applications 4/2015

01.11.2015 | Theoretical Advances

Automatic segmentation of brain MRI through stationary wavelet transform and random forests

verfasst von: Mohamed Mokhtar Bendib, Hayet Farida Merouani, Fatma Diaba

Erschienen in: Pattern Analysis and Applications | Ausgabe 4/2015

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Abstract

This paper introduces a new brain Magnetic Resonance Imaging segmentation framework that combines a powerful multiresolution/multiscale image analysis technique with a robust weakly used ensemble learning paradigm. Firstly, the image is proceeded with the anisotropic diffusion filter to reduce the noise. Then, Stationary Wavelet Transform (SWT) is applied to get multiresolution/multiscale texture information. During the SWT stage, three levels of decomposition are used and four statistical features are computed around every voxel of each resulting sub-band. The feature extraction step allows to describe each voxel through a feature vector of 60 dimensions. Finally, the extracted features are used to feed a Random Forest classifier. To train and test this classifier, we make use of the Internet Brain Segmentation Repository database. The achieved results showed that our system outperforms other state of art methods for the segmentation of Gray Matter, White Matter, and Cerebrospinal Fluid.
Vorheriger Artikel Constructing -graphs for subspace learning via recurrent neural networks
Nächster Artikel Combining local and global learners in the pairwise multiclass classification

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Metadaten
Titel
Automatic segmentation of brain MRI through stationary wavelet transform and random forests
verfasst von
Mohamed Mokhtar Bendib
Hayet Farida Merouani
Fatma Diaba
Publikationsdatum
01.11.2015
Verlag
Springer London
Erschienen in
Pattern Analysis and Applications / Ausgabe 4/2015
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI
https://doi.org/10.1007/s10044-014-0373-y

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