Classification, Dimensionality Reduction, and Maximally Discriminatory Visualization of a Multicentre 1H-MRS Database of Brain Tumors

The combination of an Artificial Neural Network classifier, a feature selection process, and a novel linear dimensionality reduction technique that provides a data projection for visualization and which preserves completely the class discrimination achieved by the classifier, is applied in this study to the analysis of an international, multi-centre 1H-MRS database of brain tumors. This combination yields results that are both intuitively interpretable and very accurate. The method as a whole remains simple enough as to allow its easy integration in existing medical decision support systems.

[1]  Carles Arús,et al.  Automated classification of short echo time in in vivo 1H brain tumor spectra: A multicenter study , 2003, Magnetic resonance in medicine.

[2]  H. P. Friedman,et al.  On Some Invariant Criteria for Grouping Data , 1967 .

[3]  Paulo J. G. Lisboa,et al.  Handling outliers in brain tumour MRS data analysis through robust topographic mapping , 2006, Comput. Biol. Medicine.

[4]  K. Gabriel,et al.  The biplot graphic display of matrices with application to principal component analysis , 1971 .

[5]  Kenneth W. Bauer,et al.  Feature saliency measures , 1997 .

[6]  A. W. Simonetti,et al.  Development of a decision support system for diagnosis and grading of brain tumours using in vivo magnetic resonance single voxel spectra , 2006, NMR in biomedicine.

[7]  Josef Kittler,et al.  Choosing an Optimal Neural Network Size to aid a Search Through a Large Image Database , 1998, BMVC.

[8]  Sabine Van Huffel,et al.  The effect of combining two echo times in automatic brain tumor classification by MRS , 2008, NMR in biomedicine.

[9]  Tamás D. Gedeon,et al.  An improved technique in porosity prediction: a neural network approach , 1995, IEEE Trans. Geosci. Remote. Sens..

[10]  Josep M. Sopena,et al.  Performing Feature Selection With Multilayer Perceptrons , 2008, IEEE Transactions on Neural Networks.

[11]  M. Julià-Sapé,et al.  A Multi-Centre, Web-Accessible and Quality Control-Checked Database of in vivo MR Spectra of Brain Tumour Patients , 2006, Magnetic Resonance Materials in Physics, Biology and Medicine.

[12]  G. L. Tarr,et al.  Multi-layered feedforward neural networks for image segmentation , 1992 .

[13]  Wright-Patterson Afb,et al.  Feature Selection Using a Multilayer Perceptron , 1990 .

[14]  R. Stephenson.  A and V , 1962, The British journal of ophthalmology.

[15]  Steven K. Rogers,et al.  Bayesian selection of important features for feedforward neural networks , 1993, Neurocomputing.

[16]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[17]  John W. Sammon,et al.  A Nonlinear Mapping for Data Structure Analysis , 1969, IEEE Transactions on Computers.

[18]  Alain Rakotomamonjy,et al.  Variable Selection Using SVM-based Criteria , 2003, J. Mach. Learn. Res..

[19]  James H. Wikel,et al.  The use of neural networks for variable selection in QSAR , 1993 .

[20]  Kevin Barraclough,et al.  I and i , 2001, BMJ : British Medical Journal.

[21]  Teuvo Kohonen,et al.  Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.