E. Ackerstaff, B. R. Pflug, and J. B. Nelson, Detection of Increased Choline Compounds with Proton Nuclear Magnetic Resonance Spectroscopy Subsequent to Malignant Transformation of Human Prostatic Epithelial Cells, Cancer Res, issue.9, pp.61-3599, 2001.

R. Bourne, P. Katelaris, and S. Danieletto, Detection of prostate cancer by magnetic resonance imaging and spectroscopy in vivo, ANZ Journal of Surgery, vol.4, issue.8, pp.73-666, 2003.
DOI : 10.1081/CNV-100103849

L. C. Costello and R. B. Franklin, Citrate metabolism of normal and malignant -1 MR spectroscopic imaging

J. Kurhanewicz, D. B. Vigneron, and H. Hricak, Three-dimensional H-1 MR spectroscopic imaging of the in situ human prostate with high (0.24-0.7-cm3) spatial resolution., Radiology, vol.198, issue.3, pp.198-795, 1996.
DOI : 10.1148/radiology.198.3.8628874

J. Kurhanewicz, D. B. Vigneron, and H. Hricak, Prostate cancer: metabolic response to cryosurgery as detected with 3D H Radiology, pp.489-496, 1996.

J. E. Mcneal, The zonal anatomy of the prostate, The Prostate, vol.5, issue.1, pp.35-49, 1981.
DOI : 10.1002/pros.2990020105

J. H. Steyn and F. W. Smith, Nuclear Magnetic Resonance Imaging of the Prostate, British Journal of Urology, vol.13, issue.6, pp.726-734, 1982.
DOI : 10.1111/j.1464-410X.1982.tb13634.x

H. Hricak, R. D. William, and D. B. Spring, Anatomy and pathology of the male pelvis by magnetic resonance ima, pp.1101-1111

P. Y. Poon, R. W. Mccallum, and M. M. Henkelman, Magnetic resonance imaging of 10, and Steyn at 1.7 and 3.4 MHz in the diagnosis, pp.350-354, 1985.

L. O. Sillerud, K. R. Halliday, and R. H. Griffey, In Vivo13C NMR Spectroscopy of the Human Prostate, Magnetic Resonance in Medicine, vol.7, issue.2, pp.224-254, 1988.
DOI : 10.1002/mrm.1910080213

M. J. Lynch and J. K. Nicholson, Proton MRS of human prostatic fluid: correlations between citrate, spermine, and myo-inositol levels and changes with disease. The Prostate, pp.248-253, 1997.

J. M. Garcìa-segura, M. Sánchez-chapado, and C. Ibarburen, In vivo proton magnetic resonance spectroscopy of diseased prostate: spectroscopic features of malignant versus benign pathology, Magnetic Resonance Imaging, vol.17, issue.5, pp.755-765, 1999.
DOI : 10.1016/S0730-725X(99)00006-5

X. Z. Wang, B. Wang, and Z. Q. Gao, 1H-MRSI of prostate cancer: The relationship between metabolite ratio and tumor proliferation, European Journal of Radiology, vol.73, issue.2, pp.345-51
DOI : 10.1016/j.ejrad.2008.10.035

R. Nagarajan, A. M. Gomez, and S. S. Raman, Correlation of endorectal 2D JPRESS findings with pathological Gleason scores in prostate cancer patients, NMR in Biomedicine, vol.59, issue.5, pp.257-61, 2010.
DOI : 10.1002/nbm.1446

M. Schmuecking, C. Boltze, and H. Geyer, Dynamic MRI and CAD vs. Choline MRS: Where is the detection level for a lesion characterisation in prostate cancer?, Lactate and alanine as metabolic biomarkers 1, pp.814-838, 2009.
DOI : 10.1002/cncr.11927

A. R. Tate, J. R. Griffiths, and F. A. Howe, Differentiating types of human brain tumours by MRS. A comparison of pre-processing methods and echo times. in ISMRM01 QUALITY: quantification improvement by converting lineshapes to the Lorentzian type, Magn Reson Med, issue.3, pp.13-343, 1990.

A. R. Tate, J. R. Griffiths, and I. Martinez-perez, Towards a method for automated classification of1H MRS spectra from brain tumours, NMR in Biomedicine, vol.379, issue.4-5, pp.177-91, 1998.
DOI : 10.1002/(SICI)1099-1492(199806/08)11:4/5<177::AID-NBM534>3.0.CO;2-U

C. M. Bishop, Pattern Recognition and Machine Learning Information Science and Statistics, Jordan M., Kleinberg J., and Schölkopf B, vol.1, p.738, 2007.

A. R. Tate, C. Majós, and A. Moreno, Automated classification of short echo time in in vivo 1 H brain tumor spectra: A multice Resonance in Medicine, p.49, 2003.

W. W. Pijnappel, A. Van-den-boogaart, and R. De-beer, SVD-based quantification of magnetic resonance signals, Journal of Magnetic Resonance (1969), vol.97, issue.1, pp.122-134, 1969.
DOI : 10.1016/0022-2364(92)90241-X

A. Devos, L. Lukas, and J. A. Suykens, Classification of brain tumours using short echo time 1H MR spectra, Journal of Magnetic Resonance, vol.170, issue.1, pp.164-175, 2004.
DOI : 10.1016/j.jmr.2004.06.010

A. Devos, A. W. Simonetti, and M. Van-der-graaf, The use of multivariate MR imaging intensities versus metabolic data from MR spectroscopic imaging for brain tumour classification, Journal of Magnetic Resonance, vol.173, issue.2, pp.218-228, 2005.
DOI : 10.1016/j.jmr.2004.12.007

L. Lukas, A. Devos, and J. A. Suykens, Brain tumor classification based on long echo proton MRS signals, Artificial Intelligence in Medicine, vol.31, issue.1, pp.31-73, 2004.
DOI : 10.1016/j.artmed.2004.01.001

V. Vapnik, The Nature of Statistical Learning Theory, 1995.

U. Klose, In vivo proton spectroscopy in presence of eddy currents, Magnetic Resonance in Medicine, vol.80, issue.1, pp.26-30, 1990.
DOI : 10.1002/mrm.1910140104

B. M. Kelm, B. H. Menze, and C. M. Zechmann, Automated estimation of tumor nance spectroscopic imaging, pp.44-51

B. D. Marx and . Eilers, Generalized Linear Regression on Sampled Signals and Curves: A P-Spline Approach, Technometrics, vol.52, issue.1, 1999.
DOI : 10.1007/BF02162161

L. Breiman, Random Forests, Machine Learning, pp.5-32, 2001.

H. Ratiney, M. Sdika, and Y. Coenradie, Time-domain semi-parametric estimation based on a metabolite basis set, NMR in Biomedicine, vol.13, issue.1, pp.150-159, 2005.
DOI : 10.1002/nbm.895
URL : https://hal.archives-ouvertes.fr/hal-00443422

S. Noworolski, R. Henry, and D. Vigneron, Dynamic contrast-enhanced MRI in normal and abnormal prostate tissues as defined by biopsy, MRI, and 3D MRSI, Magnetic Resonance in Medicine, vol.15, issue.2, pp.249-255, 2005.
DOI : 10.1002/mrm.20374

P. Swindle, S. Mccredie, and P. Russell, Pathologic Characterization of Human Prostate Tissue with Proton MR Spectroscopy, Radiology, vol.228, issue.1, p.1, 2003.
DOI : 10.1148/radiol.2281011808

B. D. Marx, Iteratively Reweighted Partial Least Squares Estimation for Generalized Linear Regression, Technometrics, vol.58, issue.4, 1996.
DOI : 10.1137/0905052

L. Van-den-boogaart, A. , V. Huffel, S. W. De-beer, R. Luyten et al., Improved Method for Accurate and Efficient Quantification of MRS Data with Use of Prior Knowledge Accurate quantification of in vivo 31P NMR signals using the variable projection method and prior knowledge, Magn Reson Med, vol.39, issue.61, pp.92-100, 1988.

S. Wold, A. Ruhe, and H. Wold, The Collinearity Problem in Linear Regression. The Partial Least Squares (PLS) Approach to Generalized Inverses, SIAM Journal on Scientific and Statistical Computing, vol.5, issue.3, pp.735-743, 1984.
DOI : 10.1137/0905052

A. Hyvärinen and E. Oja, Independent Component Analysis : Algorithms and Applic ns-5): p. 411-430. 43. pid recovery of constituent spectra in magnetic resonance chemical shift imaging 44, Neural Networks, vol.13, issue.4, 2000.

P. Tiwari, M. Rosen, and A. Madabhushi, A hierarchical spectral clustering and nonlinear dimensionality reduction scheme for detection of prostate cancer from magnetic resonance spectroscopy (MRS) Medical Physics, pp.3927-3939, 2009.

A. Madabhushi, M. D. Feldman, and D. N. Metaxas, Automated detection of prostatic adenocarcinoma from high-resolution ex vivo MRI, IEEE Transactions on Medical Imaging, vol.24, issue.12, pp.24-1611, 2005.
DOI : 10.1109/TMI.2005.859208

P. Sajda, S. Du, and T. R. Brown, Nonnegative Matrix Factorization for Rapid Recovery of Constituent Spectra in Magnetic Resonance Chemical Shift Imaging of the Brain, IEEE Transactions on Medical Imaging, vol.23, issue.12, pp.1453-65, 2004.
DOI : 10.1109/TMI.2004.834626

L. Matulewicz, K. L. Zakian, and A. Shukla-dave, A pattern recognition model for automatic classification of 1H MRSI voxels in the prostate. in ISMRM, 2009.

S. Wold, H. Antti, and F. Lindgren, Orthogonal signal correction of nearinfrared spectra, pp.175-185, 1998.

P. Tiwari, A. Madabhushi, and M. Rosen, A Hierarchical Unsupervised Spectral Clustering Scheme for Detection of Prostate Cancer from Magnetic Resonance Spectroscopy (MRS), Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv, pp.278-86, 2007.
DOI : 10.1007/978-3-540-75759-7_34

P. Tiwari, M. Rosen, and A. Madabhushi, Consensus-Locally Linear Embedding (C-LLE): Application to Prostate Cancer Detection on Magnetic Resonance Spectroscopy, Med Image Comput Comput Assist Interv, issue.11 2, pp.330-338, 2008.
DOI : 10.1007/978-3-540-85990-1_40

J. B. Macqueen, Some methods of classification and analysis of multivariate observations, Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, 1967.

J. Shi and J. Malik, Normalized cuts and image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.8, pp.888-905, 2000.

S. T. Roweis and L. K. Saul, Nonlinear Dimensionality Reduction by Locally Linear Embedding, Science, vol.290, issue.5500, p.2323, 2000.
DOI : 10.1126/science.290.5500.2323

K. L. Zakian, K. Sircar, and H. Hricak, Correlation of Proton MR Spectroscopic Imaging with Gleason Score Based on Step-Section Pathologic Analysis after Radical Prostatectomy, Radiology, vol.234, issue.3, pp.804-818, 2005.
DOI : 10.1148/radiol.2343040363

P. C. Lauterbur, Image Formation by Induced Local Interactions: Examples Employing Nuclear Magnetic Resonance, Nature, vol.178, issue.5394, pp.190-191, 1973.
DOI : 10.1038/242190a0

T. W. Scheenen, G. Gambarota, and E. Weiland, Optimal timing for in vivo 1H-MR spectroscopic imaging of the human prostate at 3T, pp.1268-1274, 2005.

J. A. Jung, F. V. Coakley, and D. B. Vigneron, Prostate Depiction at Endorectal MR Spectroscopic Imaging: Investigation of a Standardized Evaluation System, Radiology, vol.233, issue.3, pp.701-708, 2004.
DOI : 10.1148/radiol.2333030672

A. Antoniadis, J. Bigot, S. L. Provencher, and S. W. , Peaks detection and alignment for mass Estimation of metabolite concentrations from localized in vivo proton NMR spectra, Magnetic Resonance in Medicine, issue.6, pp.30-672, 1993.

B. L. Neff, J. L. Ackerman, and J. S. Waugh, Fully automatic software correction of fourier transform NMR spectra, Journal of Magnetic Resonance (1969), vol.25, issue.2, pp.335-340, 1969.
DOI : 10.1016/0022-2364(77)90028-2

A. G. Marshall, Dispersion vs. absorption (DISPA): A magic circle for spectroscopic line shape analysis, Chemometrics and Intelligent Laboratory Systems, vol.3, issue.4, pp.261-275, 1988.
DOI : 10.1016/0169-7439(88)80027-3

A. G. Marshall and D. C. , Dispersion versus absorption: spectral line shape analysis for radiofrequency and microwave spectrometry, Analytical Chemistry, vol.50, issue.6, pp.756-763, 1978.
DOI : 10.1021/ac50027a023

D. E. Brown, T. W. Campbell, and R. N. Moore, Automated phase correction of FT NMR spectra by baseline optimization, Journal of Magnetic Resonance (1969), vol.85, issue.1, pp.15-23, 1969.
DOI : 10.1016/0022-2364(89)90315-6

J. J. Van-vaals and P. H. Van-gerwen, Novel methods for automatic phase correction of NMR spectra, Journal of Magnetic Resonance (1969), vol.86, issue.1, pp.127-147, 1969.
DOI : 10.1016/0022-2364(90)90216-V

A. Heuer, A new algorithm for automatic phase correction by symmetrizing lines, Journal of Magnetic Resonance (1969), vol.91, issue.2, pp.241-253, 1969.
DOI : 10.1016/0022-2364(91)90189-Z

L. Chen, Z. Weng, and L. Goh, An efficient algorithm for automatic phase correction of NMR spectra based on entropy minimization, Journal of Magnetic Resonance, vol.158, issue.1-2, pp.164-168, 2002.
DOI : 10.1016/S1090-7807(02)00069-1

L. Bretthorst and G. , Automatic phasing of MR images. Part I: Linearly 67

C. E. Shannon, A Mathematical Theory of Communication. A Mathematical Theory of Communication, pp.379-423623, 1948.

J. Jung and B. , Quantitative 1H-magnetic resonance uman brain: Influence of composition and parameterization 73. tabolite concentrations from localized in ra, Magn Reson Med, issue.6, pp.30-672, 1993.

I. Marshall, J. Higinbotham, and S. Bruce, Use of voigt lineshape for quantification ofin vivo1H spectra, Magnetic Resonance in Medicine, vol.59, issue.5, pp.651-658, 1997.
DOI : 10.1002/mrm.1910370504

J. C. Lagarias, J. A. Reeds, and M. H. Wright, Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions, SIAM Journal on Optimization, vol.9, issue.1, pp.112-147, 1998.
DOI : 10.1137/S1052623496303470

J. A. Nelder and R. Mead, A Simplex Method for Function Minimization, The Computer Journal, vol.7, issue.4, pp.308-313, 1965.
DOI : 10.1093/comjnl/7.4.308

V. Mazet, Développement de méthodes de traitement de signaux spectroscopiques : estimation de la ligne de base et du spectre de raies, in Automatique, traitement du signal et génie informatique, p.164, 2005.

L. Hofmann, Quantitative1H-magnetic resonance spectroscopy of human brain: Influence of composition and parameterization of the basis set in linear combination model-fitting, Magnetic Resonance in Medicine, vol.594, issue.3, pp.440-453, 2002.
DOI : 10.1002/mrm.10246

R. Bartha, D. J. Drost, and P. C. Williamson, Factors affecting the quantification of short echoin-vivo1H MR spectra: prior knowledge, peak elimination, and filtering, NMR in Biomedicine, vol.33, issue.4, pp.205-221, 1999.
DOI : 10.1002/(SICI)1099-1492(199906)12:4<205::AID-NBM558>3.0.CO;2-1

C. A. Lieber and A. Mahadevan-jansen, Automated Method for Subtraction of Fluorescence from Biological Raman Spectra, Applied Spectroscopy, vol.57, issue.11, pp.1363-1370, 2003.
DOI : 10.1366/000370203322554518

S. W. Provencher and R. O. Duda, Estimation of me vivo proton NMR spect 76, John Wil rt P.E. and Stork D.G., Pattern Classification. second ed, vol.1, issue.654, 2001.

A. Aizerman, E. M. Braverman, and L. I. Rozoner, Theoretical foundations of the potential function method in pattern recognition learning. Automation and 79, pp.81-87

M. Sordo, H. Buxton, and D. Watson, A hybrid approach to breast cancer diagnosis. Practical applications of computational intelligence techniques, pp.299-330, 2001.

V. Guigue, A. Rakotomamonjy, and S. Canu, SVM et k-ppv pour la reconnaissance d'émotions

I. J. Bakken, D. Axelson, and K. A. Kvistad, Applications of neural network analyses to in vivo 1H magnetic resonance spectroscopy of epilepsy patients, Epilepsy Research, vol.35, issue.3, pp.245-252, 1999.
DOI : 10.1016/S0920-1211(99)00019-4

S. Chaplot, L. M. Patnaik, and N. R. Jagannathan, Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network, Biomedical Signal Processing and Control, vol.1, issue.1, pp.86-92, 2006.
DOI : 10.1016/j.bspc.2006.05.002

G. Hagberg, From magnetic resonance spectroscopy to classification of tumors. A review of pattern recognition methods, NMR in Biomedicine, vol.3, issue.4-5, pp.148-56, 1998.
DOI : 10.1002/(SICI)1099-1492(199806/08)11:4/5<148::AID-NBM511>3.0.CO;2-4

M. Hilario, A. Kalousis, and M. Muller, Machine learning approaches to lung cancer prediction from mass spectra, PROTEOMICS, vol.3, issue.9, pp.1716-1725, 2003.
DOI : 10.1002/pmic.200300523

Y. Hiltunen, J. Kaartinen, and J. Pulkkinen, Quantification of Human Brain Metabolites from in Vivo1H NMR Magnitude Spectra Using Automated Artificial Neural Network Analysis, Journal of Magnetic Resonance, vol.154, issue.1, pp.1-5, 2002.
DOI : 10.1006/jmre.2001.2457

J. C. Lindon, E. Holmes, and J. K. Nicholson, Pattern recognition methods and applications in biomedical magnetic resonance, Progress in Nuclear Magnetic Resonance Spectroscopy, vol.39, issue.1, pp.1-40, 2001.
DOI : 10.1016/S0079-6565(00)00036-4

B. D. Hudson, D. C. Whitley, and A. Browne, Extraction of comprehensi logical rules from neural networks. application of TREPAN in bio and chemoinformatics, Croatica chemica acta, vol.78, issue.4, pp.557-561, 2005.

J. Kaartinen, S. Mierisova, and J. M. Oja, Automated Quantification of Human Brain Metabolites by Artificial Neural Network Analysis fromin VivoSingle-Voxel1H NMR Spectra, Journal of Magnetic Resonance, vol.134, issue.1, pp.176-179, 1998.
DOI : 10.1006/jmre.1998.1477

O. Valenzuela, I. Rojas, and F. Rojas, Automatic classification of prostate cancer using pseudo-gaussian radial basis function neural network. in ESANN, 2005.

C. Z. Ye, J. Yang, and D. Y. Geng, Fuzzy rules to predict degree of malignancy in brain glioma, Medical & Biological Engineering & Computing, vol.53, issue.2, pp.145-52, 2002.
DOI : 10.1007/BF02348118

W. S. Mcculloch and W. Pitts, A logical calculus of ideas immanent in nervous activity, Bulletin of Mathematical Biophysics, issue.5, pp.115-133, 1943.

F. Rosenblatt, Principles of Neurodynamics : perceptrons and the theory of brain mechanisms 1962

B. Irie and S. Miyake, Capabilities of Three-Layer Perceptrons, IEEE Int. Conf. Neural Networks, 1988.

C. Chang and C. Lin, LIBSVM, ACM Transactions on Intelligent Systems and Technology, vol.2, issue.3, 2001.
DOI : 10.1145/1961189.1961199

F. Smach, J. Miteran, and M. Atri, An FPGA-based accelerator for Fourier Descriptors computing for color object recognition using SVM, Journal of Real-Time Image Processing, vol.12, issue.5
DOI : 10.1007/s11554-007-0065-6
URL : https://hal.archives-ouvertes.fr/hal-00647565

A. Jain, D. P. Zongker, P. Walker, and X. Tizon, Feature selection: Evaluation, application, and small sample performance IEEE Transactions on Pattern Analysis and Machine 99, Prostate cancer metabolism assessment by essing, pp.249-258, 2007.

L. Journaux, M. Destain, and J. Miteran, Texture Classification with Generalized Fourier Descriptors in Dimensionality Reduction Context: An Overview Exploration, Artificial Neural Networks in Pattern Recognition, pp.280-291, 2008.
DOI : 10.1007/978-3-540-69939-2_27

Y. Lu, I. Cohen, and S. X. Zhou, Feature selection using principal feature analysis, Proceedings of the 15th international conference on Multimedia , MULTIMEDIA '07, 2007.
DOI : 10.1145/1291233.1291297

P. Walker, X. Tizon, and S. Parfait, Evaluation of Early Response to Temozolomide and Radiotherapy with Magnetic Resonance Imaging and Proton Magnetic Resonance Spectroscopy in Human Glioma Models in Nude Rats. in AACR. 2008. Chicago. 100. Pro proton magnetic resonance spectroscopy (1H-MRS) and high resolution magic angle spinning spectroscopy (1H-HRMAS) in experimental rat models, 2009.