Abstract
In this chapter, an initial investigation of adapting the basic cuckoo search algorithm (BCSA) for the orientation distribution function (ODF) is presented. This chapter aims to extract the maxima of the ODF using BCSA, namely, CSA-ODF.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abbasloo, A., Wiens, V., Hermann, M., & Schultz, T. (2016). Visualizing tensor normal distributions at multiple levels of detail. IEEE Transactions on Visualization and Computer Graphics, 22(1), 975–984.
Abualigah, L. M., Sawaie, A. M., Khader, A. T., Rashaideh, H., Al-Betar, M. A., & Shehab, M. (2017). \(\beta \)-hill climbing technique for the text document clustering. New Trends in Information Technology, 60.
Aganj, I., Lenglet, C., & Sapiro, G. (2010). ODF maxima extraction in spherical harmonic representation via analytical search space reduction. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 84–91). Springer.
Alexander, D. C., & Barker, G. J. (2005). Optimal imaging parameters for fiber-orientation estimation in diffusion MRI. Neuroimage, 27(2), 357–367.
Bloy, L., & Verma, R. (2008). On computing the underlying fiber directions from the diffusion orientation distribution function. Medical Image Computing and Computer-Assisted Intervention-MICCAI, 2008, 1–8.
Descoteaux, M., Deriche, R., Bihan, D. L., Mangin, J.-F, & Poupon, C. (2009). Diffusion propagator imaging: using laplaces equation and multiple shell acquisitions to reconstruct the diffusion propagator. In Information processing in medical imaging (pp. 1–13). Springer.
Descoteaux, M., Angelino, E., Fitzgibbons, S., & Deriche, R. (2006). Apparent diffusion coefficients from high angular resolution diffusion imaging: Estimation and applications. Magnetic Resonance in Medicine, 56(2), 395–410.
Descoteaux, M., Angelino, E., Fitzgibbons, S., & Deriche, R. (2007). Regularized, fast, and robust analytical q-ball imaging. Magnetic Resonance in Medicine, 58(3), 497–510.
Ghosh, A. K. (1977). A numerical analysis of the tensile test for sheet metals. Metallurgical and Materials Transactions A, 8(8), 1221–1232.
Jian, B., Vemuri, B. C., Özarslan, E., Carney, P. R., Mareci, T. H. (2007). A novel tensor distribution model for the diffusion-weighted MR signal. NeuroImage, 37(1), 164–176.
Laouchedi, M., Megherbi, T., Khabatti, H., Serrat, I., Deriche, Perlbarg, R. V., & Boumghar, F. O. (2014). Odf maxima computation using hill climbing algorithm. In Proceedings of the 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI) (pp. 722–725). IEEE.
Mantegna, R. N. (1994). Fast, accurate algorithm for numerical simulation of levy stable stochastic processes. Physical Review E, 49(5), 4677.
MartÃn, A., Schiavi, E., & de León, S. S. (2016). On 1-laplacian elliptic equations modeling magnetic resonance image rician denoising. Journal of Mathematical Imaging and Vision, 1–23.
Scott, A. D., Nielles-Vallespin, S., Ferreira, P. F., McGill, L.-A., Pennell, D. J., & Firmin, D. N. (2016). The effects of noise in cardiac diffusion tensor imaging and the benefits of averaging complex data. NMR in Biomedicine, 29(5), 588–599.
Shehab, M., Daoud, M. Sh., AlMimi, H. M., Abualigah, L. M., & Khader, A. T. (2019). Hybridizing cuckoo search algorithm for extracting the ODF maxima in spherical harmonic representation. International Journal of Bio-Inspired Computation, (in press).
Shehab, M., Khader, A. T., & Al-Betar, M. A. (2016). New selection schemes for particle swarm optimization. IEEJ Transactions on Electronics, Information and Systems, 136(12), 1706–1711. https://doi.org/10.1541/ieejeiss.136.1706.
Shehab, M., Khader, A. T., & Al-Betar, M. A. (2017). A survey on applications and variants of the cuckoo search algorithm. Applied Soft Computing.
Shehab, M., Khader, A. T., & Alia, M. A. (2019). Enhancing cuckoo search algorithm by using reinforcement learning for constrained engineering optimization problems. In 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT) (pp. 812–816). IEEE.
Shehab, M., Khader, A. T., & Laouchedi, M. (2017). Modified cuckoo search algorithm for solving global optimization problems. In International Conference of Reliable Information and Communication Technology (pp. 561–570). Springer.
Shehab, M., Khader, A. T., & Laouchedi, M. (2018). A hybrid method based on cuckoo search algorithm for global optimization problems. Journal of ICT, 17(3), 469–491.
Shehab, M., Khader, A. T.,&. (2018). Modified cuckoo search algorithm using a new selection scheme for unconstrained optimization problems, 14, 1.
Shehab, M., Khader, A. T., Al-Betar, M. A., & Abualigah, L. M. (2017). Hybridizing cuckoo search algorithm with hill climbing for numerical optimization problems. In 2017 8th International Conference on Information Technology (ICIT) (pp. 36–43). IEEE.
Shehab, M., Khader, A. T., Laouchedi, M., & Alomari, O. A. (2018). Hybridizing cuckoo search algorithm with bat algorithm for global numerical optimization. The Journal of Supercomputing, 1–28.
Tournier, J.-D., Calamante, F., Gadian, D. G., & Connelly, A. (2004). Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. NeuroImage, 23(3), 1176–1185.
Tuch, D. S. (2004). Q-ball imaging. Magnetic Resonance in Medicine, 52(6), 1358–1372.
Tuch, D. S., Reese, T. G., Wiegell, M. R., Makris, N., Belliveau, J. W., & Van Wedeen, J. (2002). High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity. Magnetic Resonance in Medicine, 48(4), 577–582.
Van Wedeen, J., Hagmann, P., Tseng, W.-Y. I., Reese, T. G., & Weisskoff, R. M. (2005). Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging. Magnetic resonance in Medicine, 54(6), 1377–1386.
Veraart, J., Fieremans, Els., & Novikov, D. S. (2016). Universal power-law scaling of water diffusion in human brain defines what we see with MRI. arXiv:1609.09145.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Shehab, M. (2020). Adaptive Cuckoo Search Algorithm for Extracting the ODF Maxima. In: Artificial Intelligence in Diffusion MRI. Studies in Computational Intelligence, vol 877. Springer, Cham. https://doi.org/10.1007/978-3-030-36083-2_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-36083-2_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-36082-5
Online ISBN: 978-3-030-36083-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)