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An Improved Hardware-Oriented Level Set (Hols) for Complex Image Segmentation

Souleymane Balla-Arabe*

State key Laboratory of Integrated Services Networks, Xi’an, China.

Periodicity:January - March'2020
DOI : https://doi.org/10.26634/jip.7.1.17087

Abstract

Level Set Methods (LSMs) offer many advantages comparing with traditional snake models, among others, it has the ability to automatically handle topological changes. Nevertheless, they are computationally expensive. Today's solution to this problem is the implementation using massively parallel devices such as Graphics Processing Unit (GPU), while the development of the AMD's Heterogeneous System Architecture (HSA) systems will definitively provide future effective solutions. However, for the LSM to be suitable to parallel architectures, the curve evolution should be local and almost all the LSMs use regional statistics in order to be effective. Those whouse fully local statistics suffer from a lack of accuracy in detecting boundaries without well-defined edges. In this work, we design a novel hardware-oriented LSM, adequate to future HSA systems and today's hybrid platforms. The non-local step is done by the CPU, while the fully local curve evolution is executed in the GPU. Compared with state-of-the-art methods, this method presents better results both in terms of effectiveness and efficiency. Furthermore, it allows the detection of multiclass boundaries by using only one Level Set Function (LSF) whatever the number of classes n , while the most effective multiphase LSMs would use log n LSF. 2 Intensive experiments demonstrate the high performance of the proposed framework.

Keywords

Hardware Architectures, Image Segmentation, Level Set Method, Parallel Computing.

How to Cite this Article?

Balla-Arabé, S. (2020). An Improved Hardware-Oriented Level Set (HOLS) for Complex Image Segmentation. i-manager's Journal on Image Processing , 7(1), 1-14. https://doi.org/10.26634/jip.7.1.17087

References

[1]. Kim, B., & Ye, J. C. (2019). Mumford–shah loss functional for image segmentation with deep learning. IEEE Transactions on Image Processing, 29, 1856-1866. https://doi.org/10.1109/TIP.2019.2941265

[2]. Abu, A., & Diamant, R. (2019). Enhanced fuzzy-based local information algorithm for sonar image segmentation. IEEE Transactions on Image Processing, 29, 445-460. https://doi.org/10.1109/TIP.2019.2930148

[3]. Balla-Arabé, S., & Gao, X. (2012). Image multithresholding by combining the lattice boltzmann model and a localized level set algorithm. Neuro computing, 93, 106-114. https://doi.org/10.1016/j.neucom.2012.04.019

[4]. Balla-Arabé, S., Gao, X., & Wang, B. (2013a). A fast and robust level set method for image segmentation using fuzzy clustering and lattice Boltzmann method. IEEE Transactions on Cybernetics, 43(3), 910-920. https:// doi.org/10.1109/TSMCB.2012.2218233

[5]. Balla-Arabé, S., Gao, X., Wang, B., Yang, F., & Brost, V. (2013b). Multi-kernel implicit curve evolution for selected texture region segmentation in VHR satellite images. IEEE Transactions on Geoscience and Remote Sensing, 52(8), 5183-5192. https://doi.org/10.1109/TGRS.2013.2287239

[6]. Yang, Y., Hallman, S., Ramanan, D., & Fowlkes, C. C. (2011). Layered object models for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(9), 1731-1743. https://doi.org/10.1109/ TPAMI.2011.208

[7]. Arbeláez, P., Hariharan, B., Gu, C., Gupta, S., Bourdev, L., & Malik, J. (2012, June). Semantic segmentation using regions and parts. In 2012 IEEE Conference on Computer Vision and Pattern Recognition (pp. 3378-3385). IEEE. https://doi.org/ 10.1109/ CVPR.2012.6248077

[8]. Boykov, Y., Veksler, O., & Zabih, R. (2001). Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(11), 1222-1239. https://doi.org/10.11 09/34.969114

[9]. Malik, J., Belongie, S., Leung, T., & Shi, J. (2001). Contour and texture analysis for image segmentation. International Journal of Computer Vision, 43(1), 7-27. https://doi.org/10.1023/A:1011174803800

[10]. Balla-Arabé, S., & Gao, X. (2014). Geometric active curve for selective entropy optimization. Neuro computing, 139, 65-76.https://doi.org/10.1016/ j.neucom.2013.09.058

[11]. Balla-Arabé, S., Gao, X., Ginhac, D., Brost, V., & Yang, F. (2015). Architecture-driven level set optimization: From clustering to subpixel image segmentation. IEEE Transactions on Cybernetics, 46(12), 3181-3194. https://doi.org/10.1109/TCYB.2015.2499206

[12]. Zhang, W., Wang, X., You, W., Chen, J., Dai, P., & Zhang, P. (2019). RESLS: Region and edge synergetic level set framework for image segmentation. IEEE Transactions on Image Processing, 29, 57-71. https://doi.org/10.1 109/TIP.2019.2928134

[13]. Li, C., Huang, R., Ding, Z., Gatenby, J. C., Metaxas, D. N., & Gore, J. C. (2011). A level set method for image segmentation in the presence of intensity in homogeneities with application to MRI. IEEE Transactions o n I m a g e P r o c e s s i n g , 2 0 ( 7 ) , 2 0 0 7 - 2 0 1 6 . https://doi.org/10.1109/TIP.2011.2146190

[14]. Zhu, S. C., & Yuille, A. (1996). Region competition: Unifying snakes, region growing, and Bayes/MDL for multiband image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(9), 884- 900. https://doi.org/10.1109/34.537343

[15] . Brun, C. C., Leporé, N., Pennec, X., Chou, Y. Y., Lee, A. D., De Zubicaray, G.,McMahon,K., Wright,M. J.,Gee, J. C.,& Thompson, P. M. (2010). A nonconservative l a g r a n g i a n f r a m e w o r k f o r s t a t i s t i c a l f l u i d registration—safira. IEEE Transactions on Medical Imaging, 30(2), 184-202. https://doi.org/10.1109/TMI.20 10.2067451

[16]. Nakhmani, A., & Tannenbaum, A. (2012). Selfcrossing detection and location for parametric active contours. IEEE Transactions on Image Processing, 21(7), 3150-3156. https://doi.org/10.1109/TIP.2012.2188808

[17]. Kass, M., Witkin, A., & Terzopoulos, D. (1988). Snakes: Active contour models. International Journal Of Computer Vision, 1(4), 321-331. https://doi.org/10.1007/ BF00133570

[18] . Osher, S., & Sethian, J. A. (1988). Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. Journal of Computational Physics, 79(1), 12-49. https://doi.org/10.1016/0021- 9991(88)90002-2

[19]. Malladi, R., Sethian, J. A., & Vemuri, B. C. (1993, June). Topology-independent shape modeling scheme. In Geometric Methods in Computer Vision II (Vol. 2031, pp. 246-258). International Society for Optics and Photonics. https://doi.org/10.1117/12.146630

[20] . Caselles, V., Kimmel, R., & Sapiro, G. (1997). Geodesic active contours. International Journal Of Computer Vision, 22(1), 61-79. https://doi.org/ 10.1023/A:1007979827043

[21]. Gao, X., Wang, B., Tao, D., & Li, X. (2010). A relay level set method for automatic image segmentation. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 41(2), 518-525. https://doi.org/ 10.1109/TSMCB.2010.2065800

[22]. Kichenassamy, S., Kumar, A., Olver, P., Tannenbaum, A., & Yezzi, A. (1995, June). Gradient flows and geometric active contour models. In Proceedings of IEEE International Conference on Computer Vision (pp. 810- 815). IEEE. https://doi.org/10.1109/ICCV.1995.466855

[23]. Cremers, D., Rousson, M., & Deriche, R. (2007). A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape. International Journal Of Computer Vision, 72(2), 195-215. https://doi.org/10.1007/s11263-006-8711-1

[24]. Delgado-Gonzalo, R., Thevenaz, P., Seelamantula, C. S., & Unser, M. (2011). Snakes with an ellipsereproducing property. IEEE Transactions on Image Processing, 21(3), 1258-1271. https://doi.org/10.1109/ TIP.2011.2169975

[25]. Li, C., Xu, C., Gui, C., & Fox, M. D. (2010). Distance regularized level set evolution and its application to image segmentation. IEEE Transactions on Image Processing, 19(12), 3243-3254. https://doi.org /10.1109/TIP.2010.2069690

[26]. Balla-Arabé, S., Gao, X., & Wang, B. (2013c). GPU accelerated edge-region based level set evolution constrained by 2D gray-scale histogram. IEEE Transactions on Image Processing, 22(7), 2688-2698. https://doi.org/10.1109/TIP.2013.2255304

[27]. Silveira, M., & Heleno, S. (2009). Separation between water and land in SAR images using regionbased level sets. IEEE Geo-science and Remote Sensing Letters, 6(3), 471-475. https://doi.org/10.1109/ LGRS.2009.2017283

[28]. Ayed, I. B., & Mitiche, A. (2008). A region merging prior for variational level set image segmentation. IEEE Transactions on Image Processing, 17(12), 2301-2311. https://doi.org/10.1109/TIP.2008.2006425

[29] . Chan, T. F., & Vese, L. A. (2001). Active contours without edges. IEEE Transactions on Image Processing, 10(2), 266-277. https://doi.org/10.1109/83.902291

[30]. Mumford, D., & Shah, J. (1989). Optimal approximations by piecewise smooth functions and associated variational problems. Communications on Pure and Applied Mathematics, 42(5), 577-685. https:// doi.org/10.1002/cpa.3160420503

[31]. Osher, S., Fedkiw, R., & Piechor, K. (2004). Level set methods and dynamic implicit surfaces. Applied Mechanics Reviews, 57(3), B15-B15.

[32]. Vese, L. A., & Chan, T. F. (2002). A multiphase level set framework for image segmentation using the Mumford and Shah model. International Journal of Computer Vision, 50(3), 271-293. https://doi.org/10.1023/A:10208 4308076

[33]. Chen, Y., Tagare, H. D., Thiruvenkadam, S., Huang, F., Wilson, D., Gopinath, K. S., Briggs, R. W., & Geiser, E. A. (2002). Using prior shapes in geometric active contours in a variational framework. International Journal of Computer Vision, 50(3), 315-328. https://doi.org/ 10.1023/A:1020878408985

[34]. Li, C., Kao, C. Y., Gore, J. C., & Ding, Z. (2008). Minimization of region-scalable fitting energy for image segmentation. IEEE Transactions on Image Processing, 17(10), 1940-1949. https://doi.org/10.1109/TIP.2008.2 002304

[35] . Martin, D., Fowlkes, C., Tal, D., & Malik, J. (2001, July). A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In Proceedings Eighth IEEE International Conference on Computer Vision (Vol. 2, pp. 416-423). IEEE. https://doi.org/10.1109/ICCV.2001.937655

[36] . Succi, S. (2001). The Lattice Boltzmann Equation: For Fluid Dynamics and Beyond. Oxford University Press.

[37] . Zhao, Y. (2008). Lattice Boltzmann based PDE solver on the GPU. The Visual Computer, 24(5), 323-333. https://doi.org/10.1007/s00371-007-0191-y

[38]. He, X., & Luo, L. S. (1997). Lattice Boltzmann model for the incompressible Navier–Stokes equation. Journal of Statistical Physics, 88(3-4), 927-944. https://doi.org/ 10.1023/B:JOSS.0000015179.12689.e4

[39] . Bhatnagar, P. L., Gross, E. P., & Krook, M. (1954). A model for collision processes in gases. I. Small amplitude processes in charged and neutral one-component systems. Physical Review, 94(3), 511-525. https://doi.org/ 10.1103/PhysRev.94.511

[40]. Buick, J. M., & Greated, C. A. (2000). Gravity in a lattice boltzmann model. Physical Review E, 61(5), 5307- 5320. https://doi.org/10.1103/PhysRevE.61.5307

[41]. Shan, X., & Doolen, G. (1995). Multicomponent lattice-Boltzmann model with interparticle interaction. Journal of Statistical Physics, 81(1-2), 379-393. https:// doi.org/10.1007/BF02179985

[42]. Ginzbourg, I., & Adler, P. M. (1994). Boundary flow condition analysis for the three-dimensional lattice Boltzmann model. Journal De Physique II, 4(2), 191-214. https://doi.org/10.1051/jp2:1994123

[43]. Chapman, S., Cowling, T. G., & Burnett, D. (1990). The mathematical theory of non-uniform gases: an account of the kinetic theory of viscosity, thermal conduction and diffusion in gases. Cambridge University Press.

[44]. Chen, Y., Yan, Z., & Chu, Y. (2007, May). Cellular automata based level set method for image segmentation. In 2007 IEEE/ICME International Conference on Complex Medical Engineering (pp. 171- 174). IEEE. https://doi.org/10.1109/ICCME.2007.4381715

[45]. Balla-Arabe, S., Wang, B., & Gao, X. (2011, December). Level set region based image segmentation using lattice Boltzmann method. In 2011 Seventh International Conference on Computational Intelligence and Security (pp. 1159-1163). IEEE. https://doi.org/ 10.1109/CIS.2011.257

[46]. Salah, M. B., Mitiche, A., & Ayed, I. B. (2010). Multiregion image segmentation by parametric kernel graph cuts. IEEE Transactions on Image Processing, 20(2), 545-557. https://doi.org/10.1109/TIP.2010.2066982

[47]. Hagan, A., & Zhao, Y. (2009, November). Parallel 3D image segmentation of large data sets on a GPU cluster. In International Symposium on Visual Computing (pp. 960-969). Springer, Berlin, Heidelberg. https://doi.org/ 10.1007/978-3-642-10520-3_92

[48]. Gibou, F., & Fedkiw, R. (2005, January). A fast hybrid th k-means level set algorithm for segmentation. In 4 Annual Hawaii International Conference on Statistics and Mathematics (pp. 281-291).

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