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A Re-Learning Based Post-Processing Step For Brain Tumor Segmentation From Multi-Sequence Images
Naouel Boughattas, Maxime Berar, Kamel Hamrouni, Su Ruan
Pages - 50 - 62     |    Revised - 30-04-2016     |    Published - 01-06-2016
Volume - 10   Issue - 2    |    Publication Date - June 2016  Table of Contents
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KEYWORDS
Cerebral MRI, Tumor, Segmentation, Feature Selection, Multiclass, Classification, Multiple Kernel Learning, Multimodal.
ABSTRACT
We propose a brain tumor segmentation method from multi-spectral MRI images. The method is based on classification and uses Multiple Kernel Learning (MKL) which jointly selects one or more kernels associated to each feature and trains SVM (Support Vector Machine).

First, a large set of features based on wavelet decomposition is computed on a small number of voxels for all types of images. The most significant features from the feature base are then selected and a classifier is then learned. The images are segmented using the trained classifier on the selected features. In our framework, a second step called re-learning is added. It consists in training again a classifier from a reduced part of the training set located around the segmented tumor in the first step. A fusion of both segmentation procures the final results.

Our algorithm was tested on the real data provided by the challenge of Brats 2012. This dataset includes 20 high-grade glioma patients and 10 low-grade glioma patients. For each patient, T1, T2, FLAIR, and post-Gadolinium T1 MR images are available. The results show good performances of our method.
1 CiteSeerX 
2 refSeek 
3 Scribd 
4 SlideShare 
5 PdfSR 
A. Rakotomamonjy, F. Bach, S. Canu and Y. Grandvalet. “Simplemkl”. Journal of Machine Learning Research 2008;9(11).
B. Menze, A. Jakab, S. Bauer, M. Prastawa, M. Reye and K. Van Leem-put. “The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) ”. Tech. Rep, 2012.
E.A. El-Dahshan, H.M. Mohsen, K. Revett and A.M. Salem. “Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm”. Expert Syst Appl 2014;41(11).
G.R Lanckriet, N. Cristianini, P. Bartlett, L.E. Ghaoui and M.I Jordan. “Learning the kernel matrix with semidefinite programming”. The Journal of Machine Learning Research 2004;5:27-72.
Geremia, E., Clatz, O., Menze, B., Konukoglu, E., Criminisi, A., Ayache, N.. Spatial decision forests for ms lesion segmentation in multi-channel magnetic resonance images. NeuroImage 2011;57(2):378-390.
Hamamci, A., Kucuk, N., Karaman, K., Engin, K., Unal, G.. Tumor-cut: Segmentation of brain tumors on contrast enhanced mr images for radiosurgery applications. Medical Imaging, IEEE Transactions on 2012;31(3):790-804.
L. Wang . “Feature selection with kernel class separability”. Pattern Analysis and Machine Intelligence, IEEE Transactions on 2008;30(9):1534- 1546.
N. Boughattas, M. Berar, K. Hamrouni, an S. Ruan. “Brain tumor segmentation from multiple MRI sequences using multiple kernel learning ”. ICIP 2014: 1887-1891.
N. Gordillo, E. Montseny and P. Sobrevilla. “State of the art survey on {MRI} brain tumor segmentation. Magnetic Resonance Imaging ”.2013;31(8):1426 - 1438.
N. Zhang, S. Ruan, S. Lebonvallet, Q. Liao and Y. Zhu. “Kernel feature selection to fuse multi-spectral mri images for brain tumor segmentation”. Computer Vision and Image Understanding 2011;115(2):256 - 269.
S. Bauer, L.P. Nolte and M. Reyes. “Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization”. Medical Image Computing and Computer-Assisted Intervention-MICCAI 2011 :354-361.
S. Bauer, R. Wiest, L.P. Nolte and M. Reyes. “A survey of mri-based medical image analysis for brain tumor studies”. Physics in Medicine and Biology 2013;58(13):R97.
T. Ojala, M. Pietikainen and T. Maenpaa. “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns”. Pattern Analysis and Machine Intelligence, IEEE Transactions on 2002;24(7):971- 987.
X.W. Chen and M. Wasikowski. Fast: “A roc-based feature selection metric for small samples and imbalanced data classification problems”. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD '08; New York, NY, USA: ACM. ISBN 978-1-60558-193-4; 2008, p. 124-132.
Zikic, D., Glocker, B., Konukoglu, E., Criminisi, A., Demiralp, C., Shotton, J., et al. Decision forests for tissue-speci_c segmentation of high-grade gliomas in multi-channel mr. In: Medical Image Computing and Computer-Assisted Intervention{MICCAI 2012. Springer Berlin Heidelberg; 2012, p. 369-376.
Dr. Naouel Boughattas
University of Tunis El Manar - Tunisia
naouel.boughattas@gmail.com
Mr. Maxime Berar
University of Rouen - France
Professor Kamel Hamrouni
University of Tunis El Manar - Tunisia
Professor Su Ruan
University of Rouen - France