Brain Tumor Segmentation from Multi-Spectral Images Using Multi-Kernel Learning

Naouel Boughattas, Maxime Berar, Kamel Hamrouni, Su Ruan

Abstract


We propose a brain tumor segmentation method from multi-spectral MRI images. The new idea is to use Multiple Kernel Learnin (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, allowing us to build a training feature base. The segmentation task is then viewed as a learning problem where only the most significant features from the feature base are selected and a classifier is then learned. The images are then segmented using the trained classifier on the selected features.

Our algorithm was tested on the real data provided by the challenge of Brats 2012. The 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. Our algorithm was compared to the resulting top methods of the challenge of Brats 2012. The results show good performances of our method.


Full Text:

PDF

References


Bauer, S., Wiest, R., Nolte, L.P., Reyes, M.. “A survey of mri-based medical image analysis for brain tumor studies”. Physics in Medicine and Biology 2013;58(13):R97.

Gordillo, N., Montseny, E., Sobrevilla, P.. “State of the art survey on {MRI} brain tumor segmentation. Magnetic Resonance Imaging ”.2013;31(8):1426 - 1438.

El-Dahshan, E.A., Mohsen, H.M., Revett, K., Salem, A.M.. “Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm”. Expert Syst Appl 2014;41(11).

Schad, L.R., Bluml, S., Zuna, I.. “Mr tissue characterization of intracranial tumors by means of texture analysis”. Magnetic Resonance Imaging 1993;11(6):889 - 896.

Hastie, T., Tibshirani, R., Friedman, J.. “The Elements of Statistical Learning”. Springer Series in Statistics; New York, NY, USA: Springer New York Inc.; 2001.

Farzinfar, M., Teoh, E.K., Xue, Z.. “A joint shape evolution approach to medical image segmentation using expectation-maximization algorithm”. Magnetic Resonance Imaging 2011;29(9):1255 - 1266.

Zhang, T., Xia, Y., Feng, D.D.. “Hidden markov random field model based brain {MR} image segmentation using clonal selection algorithm and markov chain monte carlo method”. Biomedical Signal Processing and Control 2014;12(0):10 - 18.

Ji, Z., Xia, Y., Sun, Q., Chen, Q., Feng, D.. “Adaptive scale fuzzy local gaussian mixture model for brain MR image segmentation”. Neurocomputing 2014;134:60 - 69.

Dong, F., Peng, J.. “Brain MR image segmentation based on local gaussian mixture model and nonlocal spatial regularization”. J Visual Communication and Image Representation 2014;25(5):827 - 839.

Bauer, S., Nolte, L.P., Reyes, M.. “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.

Zikic, D., Glocker, B., Konukoglu, E., Criminisi, A., Demiralp, C., Shotton, J., et al. “Decision forests for tissue-specific segmentation of high-grade gliomas in multi-channel mr”. Medical Image Computing and Computer-Assisted Intervention-MICCAI 2012. Springer Berlin Heidelberg; 2012, p. 369-376.

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.

Corso, J., Sharon, E., Dube, S., El-Saden, S., Sinha, U., Yuille, A.. “Efficient multilevel brain tumor segmentation with integrated bayesian model classification”. Medical Imaging, IEEE Transactions on 2008;27(5):629-640.

Menze, B., Jakab, A., Bauer, S., Prastawa, M., Reyes, M., Van Leem-put, K.. “The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) ”. Tech. Rep, 2012.

Chen, X.w., Wasikowski, M.. 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.

Wang, L.. “Feature selection with kernel class separability”. Pattern Analysis and Machine Intelligence, IEEE Transactions on 2008;30(9):1534- 1546.

Lanckriet, G.R., Cristianini, N., Bartlett, P., Ghaoui, L.E., Jordan, M.I.. “Learning the kernel matrix with semidefinite programming”. The Journal of Machine Learning Research 2004;5:27-72.

Zhang, N., Ruan, S., Lebonvallet, S., Liao, Q., Zhu, Y.. “Kernel feature selection to fuse multi-spectral mri images for brain tumor segmentation”. Computer Vision and Image Understanding 2011;115(2):256 - 269.

Ojala, T., Pietikainen, M., Maenpaa, T.. “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.

Rakotomamonjy, A., Bach, F., Canu, S., Grandvalet, Y.. “Simplemkl”. Journal of Machine Learning Research 2008;9(11).

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.


Refbacks

  • There are currently no refbacks.




© International Journals of Advanced Research in Computer Science and Software Engineering (IJARCSSE)| All Rights Reserved | Powered by Advance Academic Publisher.