F. Abramovich, Y. Benjamini, D. Donoho, and I. Johnstone, Adapting to unknown sparsity by controlling the false discovery rate, The Annals of Statistics, vol.34, issue.2, 2006.
DOI : 10.1214/009053606000000074

Y. Benjamini and Y. Hocheberg, Controlling the false discovery rate : a practical and poweful, approach to multiple testing, Journal of Royal Statistical Society B, vol.57, pp.289-300, 1995.

Y. Benjamini and D. Yekutieli, The control of the false discovery rate in multiple testing under dependency, The Annals of Statistics, vol.29, issue.4, pp.1165-1188, 2001.

L. Breiman, J. Friedman, O. R. , and S. C. , Classification and regression trees, 1983.

D. Donoho, CART and best-ortho-basis: a connection, The Annals of Statistics, vol.25, issue.5, pp.1870-1911, 1997.
DOI : 10.1214/aos/1069362377

J. Fan, Test of significance based on wavelet thresholding and neyman's truncation, JASA, pp.674-688, 1996.

L. Guigues, Modèles Multi-Échelles pour la Segmentation d'Images, 2003.

G. Hagberg, From magnetic resonance spectroscopy to classification of tumors, a reviewof pattern recognition methods, NMR in Biomedicine, vol.156, issue.11, p.148, 1998.

I. Y. Ingster and I. Suslina, Nonparametric Goodness-of-Fit Testing under Gaussian Model, Lecture Notes in Statistics, vol.169, 2002.
DOI : 10.1007/978-0-387-21580-8

I. H. Jermyn and H. Ishikawa, Globally optimal regions and boundaries as minimum ratio weight cycles, IEEE Transaction on pattern analysis and machine intelligence, 2001.

M. Kohler, Nonparametric estimation of piecewise smooth regression functions, Statistics & Probability Letters, vol.43, issue.1, 2003.
DOI : 10.1016/S0167-7152(98)00245-4

H. Philippe, N. Stransky, J. P. Thiery, F. Radvanyi, and E. Barillot, Analysis of array cgh data: from signal ratio to gain and loss of dna regions, Bioinformatics, issue.18, pp.20-3413, 2004.

J. Polzehl and V. Spokoiny, Adaptive weights smoothing with applications to image restoration, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.62, issue.2, pp.335-354, 2000.
DOI : 10.1111/1467-9868.00235

J. Polzehl and V. Spokoiny, Vector adaptive weights smoothing with application to mri, J.R Stat Soc B, vol.63, pp.335-354, 2001.

V. Spokoiny, Adaptative hypothesis testing using wavelets, Annals of Statistics, vol.24, issue.6, pp.2477-2498, 1996.

J. D. Storey, The positive false discovery rate: a Bayesian interpretation and the q -value, The Annals of Statistics, vol.31, issue.6, pp.2013-2035, 2003.
DOI : 10.1214/aos/1074290335

J. D. Storey, J. E. Taylor, and S. , A direct approach to false discovery rates, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.82, issue.3, pp.479-498, 2002.
DOI : 10.1111/1467-9868.00346

J. D. Storey, J. E. Taylor, and S. , Strong control, conservative point estimation and simultaneous conservative consistency of false discovery rates: a unified approach, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.73, issue.1, pp.187-205, 2004.
DOI : 10.1016/S0378-3758(99)00041-5

F. Szablo and D. Edelenyi, Développement d'une nouvelle approche d'analyse des images spectroscopiques RMN : les images nosologiques, 2001.

J. Taylor, R. Tibshirani, and B. Efron, The 'miss rate' for the analysis of gene expression data, Biostatistics, vol.6, issue.1, pp.111-117, 2005.
DOI : 10.1093/biostatistics/kxh021

B. Whitcher, A. J. Schwarz, H. Barjat, S. Smart, R. Grundy et al., Wavelet-based cluster analysis: data-driven grouping of voxel time courses with application to perfusion-weighted and pharmacological MRI of the rat brain, NeuroImage, vol.24, issue.2, pp.24-281, 2005.
DOI : 10.1016/j.neuroimage.2004.08.022

C. Zhu, S. , and A. Yuille, Region competition, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.18, issue.9, 1996.