A Non-Local Diffusion Saliency Model for Magnetic Resonance Imaging

I. Ramírez, G. Galiano, N. Malpica, E. Schiavi

Abstract

Based on previous work on image classification and recent applications of non-local non-linear diffusion equations, we propose a non-local p-laplacian variational model for saliency detection in digital images. Focusing on the range 0 < p < 1 we also consider the regularized non-convex fluxes generated by the related hyper-laplacian diffusion operators. With the aim of exploring the properties and potential applications of such non-local, non-convex operators the model is applied to Magnetic Resonace Imaging (MRI) for Fluid Attenuated Inversion Recovery image (FLAIR) modality showing promising numerical results. In this work Saliency shall be understood as the relevant, outstanding region in a FLAIR image, which is commonly the brightest part. It corresponds to a tumor and neighborhood edema. Our preliminary experiments show that the proposed model can achieve very accurate results in this modality in terms of all the considered metrics.

References

  1. Ambrosio, L., Fusco, N., and Pallara, D. (2000). Functions of bounded variation and free discontinuity problems. Oxford university press.
  2. Andreu-Vaillo, F., Mazón, J. M., Rossi, J. D., and ToledoMelero, J. J. (2010). Nonlocal diffusion problems, volume 165. American Mathematical Society.
  3. Charbonnier, P., Blanc-Féraud, L., Aubert, G., and Barlaud, M. (1997). Deterministic edge-preserving regularization in computed imaging. IEEE Transactions on image processing, 6(2):298-311.
  4. Gilboa, G. and Osher, S. (2008). Nonlocal operators with applications to image processing. Multiscale Modeling & Simulation, 7(3):1005-1028.
  5. Harel, J., Koch, C., and Perona, P. (2006). Graph-based visual saliency. In Advances in neural information processing systems, pages 545-552.
  6. Hintermüller, M. and Wu, T. (2014). A smoothing descent method for nonconvex tvˆ q-models. In Efficient Algorithms for Global Optimization Methods in Computer Vision, pages 119-133. Springer.
  7. Li, M., Zhan, Y., and Zhang, L. (2013). Nonlocal variational model for saliency detection. Mathematical Problems in Engineering, 2013.
  8. Liu, R., Cao, J., Lin, Z., and Shan, S. (2014). Adaptive partial differential equation learning for visual saliency detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3866-3873.
  9. Liu, Z., Meur, L., and Luo, S. (2013). Superpixel-based saliency detection. In 2013 14th International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS), pages 1-4. IEEE.
  10. Rudin, L. I., Osher, S., and Fatemi, E. (1992). Nonlinear total variation based noise removal algorithms. Physica D: Nonlinear Phenomena, 60(1):259-268.
  11. Rueda, A., González, F., and Romero, E. (2013). Saliency-based characterization of group differences for magnetic resonance disease classification. Dyna, 80(178):21-28.
  12. Samson, C., Blanc-Féraud, L., Aubert, G., and Zerubia, J. (1998). Image classification using a variational approach. PhD thesis, INRIA.
  13. Thota, R., Vaswani, S., Kale, A., and Vydyanathan, N. (2016). Fast 3d salient region detection in medical images using gpus. In Machine Intelligence and Signal Processing, pages 11-26. Springer.
  14. Tomasi, C. and Manduchi, R. (1998). Bilateral filtering for gray and color images. In Computer Vision, 1998. Sixth International Conference on, pages 839-846. IEEE.
  15. Wang, Y., Liu, R., Song, X., and Su, Z. (2014). Saliency detection via nonlocal l {0} minimization. In Asian Conference on Computer Vision, pages 521-535. Springer.
  16. Weickert, J. (1998). Anisotropic diffusion in image processing, volume 1. Teubner Stuttgart.
  17. Yang, Q., Tan, K.-H., and Ahuja, N. (2009). Real-time o (1) bilateral filtering. InComputer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pages 557-564. IEEE.
  18. Zhan, Y. (2011). The nonlocal-laplacian evolution for image interpolation. Mathematical Problems in Engineering, 2011.
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Paper Citation


in Harvard Style

Ramírez I., Galiano G., Malpica N. and Schiavi E. (2017). A Non-Local Diffusion Saliency Model for Magnetic Resonance Imaging . In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING, (BIOSTEC 2017) ISBN 978-989-758-215-8, pages 100-107. DOI: 10.5220/0006172101000107


in Bibtex Style

@conference{bioimaging17,
author={I. Ramírez and G. Galiano and N. Malpica and E. Schiavi},
title={A Non-Local Diffusion Saliency Model for Magnetic Resonance Imaging},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING, (BIOSTEC 2017)},
year={2017},
pages={100-107},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006172101000107},
isbn={978-989-758-215-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING, (BIOSTEC 2017)
TI - A Non-Local Diffusion Saliency Model for Magnetic Resonance Imaging
SN - 978-989-758-215-8
AU - Ramírez I.
AU - Galiano G.
AU - Malpica N.
AU - Schiavi E.
PY - 2017
SP - 100
EP - 107
DO - 10.5220/0006172101000107