GVF snake algorithm-a parallel approach

  • Abstract
  • Keywords
  • References
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  • Abstract

    Multicore architecture is an emerging computer technology where multiple processing element will be acting as independent processing cores by sharing a common memory. Digital image segmentation is a widely used medical imaging application to extract regions of interest. GVF Active Contour is a region based segmentation technique which extracts curved and irregular shaped regions by diffusing gradient vectors and by the influence of internal and external forces. This requires prior knowledge on the geometric position and anatomical structures to locate the specific region defined within an image domain. This process requires complex mathematical calculations which in turn results in the immense consumption of CPU processing time. This may adversely affect the overall performance efficiency of the process. With the advancements in multicore technology, this processing time delay can be reduced by adapting parallelization in the computation of GVF field to the specific region of interest which is to be segmented. OpenMP is a shared memory parallel programming construct, which could implement multicore parallelism with extensive and powerful APIs thereby supporting the functionalities required to attain parallelism. This article provides a high level overview of OpenMP, its effectiveness and ease of implementation in adapting parallelism to existing traditional sequential methods using instruction, data and loop level parallelism. Performance comparison could be done with sequential versions of the program written in Matlab, Java and C languages with the proposed parallelized version of OpenMP. The result is also comparable with different operating systems like Windows and Linux.

  • Keywords

    GVF, active contour snake, region based segmentation, OpenMP, API.

  • References

      [1] Alvarado R, Tapia JJ & Rolón JC, “Medical image segmentation with deformable models on graphics processing units”, The Journal of Supercomputing, Vol.68, No.1, pp.339-364, (2014).

      [2] Phillips RD, Watson LT & Wynne RH, “Hybrid image classification and parameter selection using a shared memory parallel algorithm”, Computers & Geosciences, Vol.33, No.7, pp.875-897, (2007).

      [3] Mahmoud MKA & Al-Jumaily A, “Segmentation of skin cancer images based on GVF snake”, IEEE International Conference on mechatronics and automation, pp. 216-220, (2011).

      [4] Schellmann M, Gorlatch S, Meilander D, Kosters T, Schafers K, Wubbeling F & Burger M, “Parallel medical image reconstruction from graphics processing units to grids”, Journal of Supercomputing, Vol.57, pp.151-160, (2011).

      [5] Pallippuram VK, Bhuiyan M & Smith M C, “A comparative study on GPU programming models and architectures using neural networks”, Journal of Supercomputing, Vol.61, pp.673-718, (2012).

      [6] Zheng ZY & Zhang RX, “A fast GVF snake algorithm on the GPU”, Research Journal of Applied Sciences, Engineering and Technology, Vol.4, No.24, pp.5565-5571, (2012)

      [7] Lee S & Eigenmann R, “OpenMPC: Extended OpenMP for efficient programming and tuning on GPUs”, International Journal of Computational Science and Engineering, Vol.7, No.1, (2012).

      [8] Chapman B, Jost G & Van Der Pas R, Using OpenMP: portable shared memory parallel programming, MIT press, Vol.10, (2010).

      [9] Kim W & Kim C, “Active contours driven by the salient edge energy model”, IEEE Transactions on Image Processing, Vol.22, No.4, pp.1667–1673, (2013).

      [10] Zhao J, Liang G, Yuan Z & Zhang D, “A new method of breakpoint connection using curve features for contour vectorization”, Electronics and Electrical Engineering, Vol.18, No.9, pp.79–82, (2012).

      [11] Zhao J, Chen B, Sun M, Jia W & Yuan Z, “Improved algorithm for gradient vector flow based active contour model using global and local information”, The Scientific World Journal, (2013).

      [12] Wang L, Li C, Sun Q, Xia D & Kao CY, “Active contours driven by local and global intensity fitting energy with application to brain MR image segmentation”, Computerized Medical Imaging and Graphics, Vol.33, No.7, pp.520-531, (2009).

      [13] Shen J, Fang J, Sips H & Varbanescu AL, “An application-centric evaluation of OpenCL on multi-core CPUs”, Parallel Computing, Vol.39, No.12, pp.834-850, (2013).

      [14] Karantasis KI, Polychronopoulos ED, Panourgias KT & Ekaterinaris JA, “Accelerating the simulation of brain tumor proliferation with many-core GPUs”, Journal of Computational Science, Vol.3, No.5, pp.306-313, (2012).

      [15] Wan J & Liu Y, “Hybrid MPI-OpenMP Parallelization of image reconstruction”, Journal of Software, Vol.8, No.3, pp.687-693, (2013).




Article ID: 9206
DOI: 10.14419/ijet.v7i1.1.9206

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