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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.