LSOA Optimized Curve Fitting-based 3D Reconstruction of Brain Tumor

doi:10.23940/ijpe.21.12.p6.10161026

Abstract

Abstract: Medical image processing encompasses the utilization of three dimensionally reconstructed images from CT or MRI scans to diagnose pathologies, surgical planning, and radiation dose calculation. The 3D reconstructed images help the radiologists and surgeons to better understand the complicated internal anatomy. This paper proposes a novel technique of using the curve fitting process through optimization for the three dimensional reconstruction of a brain tumor. The curve fitting process achieves accurate reconstruction by estimating the boundaries as well as the corner points of the model. The process of optimization in the curve fitting process is carried out using the new Levy Walk Trajectory based Seagull Optimization Algorithm which combines the merits of Levy walk with the metaheuristic Seagull Optimization Algorithm to obtain better accuracy. The boundary fitting is precisely done by considering the minimization of root mean square error among original and fitted boundaries. Finally, performance of the adopted method is validated over other existing schemes with respect to curve fit analysis and convergence analysis.

Key words: MRI, 3D reconstruction, brain tumor, Bezier curve, parameterization

Sushitha Susan Joseph, and Aju D. LSOA Optimized Curve Fitting-based 3D Reconstruction of Brain Tumor [J]. Int J Performability Eng, 2021, 17(12): 1016-1026.

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References

1. Lamadé W., Glombitza G., Fischer L., Chiu P., Cárdenas Sr, C.E., Thorn, M., Meinzer, H.P., Grenacher, L., Bauer, H., Lehnert, T. and Herfarth, C, The Impact of 3-dimensional Reconstructions on Operation Planning in Liver Surgery. Archives of surgery, vol. 135, no. 11, pp. 1256-1261, 2000.
2. Lamata P., Lamata F., Sojar V., Makowski P., Massoptier L., Casciaro S., Ali W., Stüdeli T., Declerck J., Elle O.J., andEdwin B.Use of the Resection Map System as Guidance during Hepatectomy. Surgical endoscopy, vol. 24, no. 9, pp. 2327-2337, 2010.
3. Jiang G.P., Zhang Y., Chen W.F. and Li S.X.Three-dimensional Reconstruction of Human Organs based on Magnetic Resonance Imaging Data. Di 1 jun yi da xue xue bao Academic Journal of the First Medical College of PLA, vol. 25, no. 1, pp. 15-17, 2005.
4. J. Jurek, M. Kociński, A. Materka, M. Elgalal,A. Majos, CNN-based Superresolution Reconstruction of 3D MR Images using Thick-slice Scans, Biocybernetics and Biomedical Engineering, vol. 40, no. 1, pp. 111-125, 2020.
5. J. Du et al., Super-resolution Reconstruction of Single Anisotropic 3D MR Images using Residual Convolutional Neural Network, Neurocomputing, vol. 392, pp. 209-220, 2020.
6. M. Agus et al., Shape Analysis of 3D Nanoscale Reconstructions of Brain Cell Nuclear Envelopes by Implicit and Explicit Parametric Representations, Comput & Graph. X, vol. 1, pp. 100004, 2019.
7. S. Ali, S. Wörz, K. Amunts, R. Eils, M. Axer,K. Rohr, Rigid and Non-rigid Registration of Polarized Light Imaging Data for 3D Reconstruction of the Temporal Lobe of the Human Brain at Micrometer Resolution, Neuroimage, vol. 181, pp. 235-251, 2018.
8. A. Bustin et al., Isotropic Reconstruction of MR Images using 3D Patch-based Self-similarity Learning,” IEEE transactions on medical imaging, vol. 37, no. 8, pp. 1932-1942, 2018.
9. H. Liang, N. Dabrowska, J. Kapur,D. S. Weller, Structure-Based Intensity Propagation for 3-D Brain Reconstruction With Multilayer Section Microscopy, IEEE transactions on medical imaging, vol. 38, no. 5, pp. 1106-1115, 2018.
10. N. Rusdi, Z. R. Yahya, N. Roslan,W. Z. A.Wan Muhamad, Reconstruction of Medical Images using Artificial Bee Colony Algorithm,” Mathematical Problems in Engineering, vol. 2018, 2018.
11. H. Abe et al., 3D Reconstruction of Brain Section Images for Creating Axonal Projection Maps in Marmosets,” Journal of neuroscience methods, vol. 286, pp. 102-113, 2017.
12. G. Dhiman and V.Kumar, Seagull Optimization Algorithm: Theory and Its Applications for Large-scale Industrial Engineering Problems, Knowledge-Based Syst., vol. 165, pp. 169-196, 2019.
13. X.-S. Yang and S. Deb, Cuckoo Search via Lévy Flights, in2009 World congress on nature & biologically inspired computing (NaBIC), 2009, pp. 210-214.
14. I. Pavlyukevich, Lévy Flights, Non-local Search and Simulated Annealing, Journal of Computational Physics, vol. 226, no. 2, pp. 1830-1844, 2007.
15. P. Barthelemy, J. Bertolotti,D. S. Wiersma, A Lévy Flight for Light, Nature, vol. 453, no. 7194, pp. 495-498, 2008.
16. R. N. Mantegna, Fast, Accurate Algorithm for Numerical Simulation of Levy Stable Stochastic Processes, Physical Review E, vol. 49, no.5, p. 4677, 1994.
17. Z. Long and K. Nagamune, A Marching Cubes Algorithm: Application for Three-dimensional Surface Reconstruction based on Endoscope and Optical Fiber,” Information, vol. 18, no. 4, pp. 1425-1437, 2015.
18. A. H. Gandomi, X.-S. Yang, S. Talatahari, and A. H. Alavi, Firefly Algorithm with Chaos, ommunications in Nonlinear Science and Numerical Simulation, vol. 18, no. 1, pp. 89-98, 2013.
19. J. McCall, Genetic Algorithms for Modelling and Optimisation, J. Comput. Journal of computational and Applied Mathematics, vol. 184, no. 1, pp. 205-222, 2005.
20. S. Mirjalili, S. M. Mirjalili,A. Lewis, Grey Wolf Optimizer, Advances in engineering software, vol. 69, pp. 46-61, 2014.
21. G.-G. Wang, S. Deb, and L. dos S. Coelho, Elephant Herding Optimization, in2015 3rd International Symposium on Computational and Business Intelligence (ISCBI), 2015, pp. 1-5.

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