Learning Adaptive and Sparse Representations of Medical Images

In this paper we discuss the impact of using algorithms for dictionary learning to build adaptive and sparse representations of medical images. The effectiveness of coding data as sparse linear combinations of the elements of an over-complete dictionary is well assessed in the medical context. Confirming what has been observed for natural images, we show the benefits of using adaptive dictionaries, directly learned from a set of training images, that better capture the distribution of the data. The experiments focus on the specific task of image denoising and produce clear evidence of the benefits obtained with the proposed approach.

[1]  David J. Field,et al.  Sparse coding with an overcomplete basis set: A strategy employed by V1? , 1997, Vision Research.

[2]  Gabriel Peyré,et al.  Sparse Modeling of Textures , 2009, Journal of Mathematical Imaging and Vision.

[3]  Michael Elad,et al.  Double Sparsity: Learning Sparse Dictionaries for Sparse Signal Approximation , 2010, IEEE Transactions on Signal Processing.

[4]  Yihong Gong,et al.  Linear spatial pyramid matching using sparse coding for image classification , 2009, CVPR.

[5]  Guillermo Sapiro,et al.  Supervised Dictionary Learning , 2008, NIPS.

[6]  Stphane Mallat,et al.  A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way , 2008 .

[7]  Michael Elad,et al.  Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries , 2006, IEEE Transactions on Image Processing.

[8]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[9]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[11]  M. Elad,et al.  $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.

[12]  Marc'Aurelio Ranzato,et al.  A Unified Energy-Based Framework for Unsupervised Learning , 2007, AISTATS.

[13]  Guillermo Sapiro,et al.  Online Learning for Matrix Factorization and Sparse Coding , 2009, J. Mach. Learn. Res..

[14]  Marc'Aurelio Ranzato,et al.  Fast Inference in Sparse Coding Algorithms with Applications to Object Recognition , 2010, ArXiv.

[15]  Michael Elad,et al.  On the Role of Sparse and Redundant Representations in Image Processing , 2010, Proceedings of the IEEE.

[16]  E. B. Andersen,et al.  Information Science and Statistics , 1986 .

[17]  Kjersti Engan,et al.  Method of optimal directions for frame design , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[18]  Heinz H. Bauschke,et al.  Fixed-Point Algorithms for Inverse Problems in Science and Engineering , 2011, Springer Optimization and Its Applications.

[19]  Rajat Raina,et al.  Efficient sparse coding algorithms , 2006, NIPS.

[20]  Hayit Greenspan,et al.  Automated and Interactive Lesion Detection and Segmentation in Uterine Cervix Images , 2010, IEEE Transactions on Medical Imaging.

[21]  Patrick L. Combettes,et al.  Proximal Splitting Methods in Signal Processing , 2009, Fixed-Point Algorithms for Inverse Problems in Science and Engineering.

[22]  S. Mallat.  A wavelet tour of signal processing , 1998 .

[23]  Rajat Raina,et al.  Self-taught learning: transfer learning from unlabeled data , 2007, ICML '07.

[24]  Joel A. Tropp,et al.  Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit , 2007, IEEE Transactions on Information Theory.

[25]  A. Bruckstein,et al.  K-SVD : An Algorithm for Designing of Overcomplete Dictionaries for Sparse Representation , 2005 .