Design and Evaluation of Parallel Processing Techniques for 3D Liver Segmentation and Volume Rendering

Mohammed Goryawala*, Magno R. Guillen**, Armando Barreto***, Ruchir Bhatt****, Seza Gulec*****, Tushar Barot******, Rekha Suthar*******, Anthony McGoron********, Malek Adjouadi*********
*-****-******** Florida International University, Department of Biomedical Engineering, Miami.
**-***-********* Florida International University, Department of Electrical & Computer Engg., Miami.
*****-******-******* Florida International University, Herbert Wertheim College of Medicine, Miami.
Periodicity:April - June'2011
DOI : https://doi.org/10.26634/jse.5.4.1444

Abstract

3D reconstruction is a task that exerts a heavy computational load. This study describes the performance results obtained on testing MatLab parallel processing toolbox to execute a three-dimensional (3-D) liver reconstruction. The FFT algorithm was tested using the parallel tool box, by changing system platform, number of workers,image size and number of images. A second set was executed keeping the hardware fixed and changing the operating system to obtain unbiased results. The third experiment set was to assess the effect of parallelization applied to a newly developed 3-D liver reconstruction algorithm. Results showed a reduction of processing time from 4.5 hours to almost 1 hour, yielding a 78% (3.5 hours) saving in computational time due to the multicore deployment. The results show that the leveraging of multicore platforms can speed up considerably the processing of medical images through the use of parallel computing tools in MatLab.

Keywords

Matlab, HPC, Parallel Computing, Distributed Computing, Performance Metrics, 3-D Reconstruction.

How to Cite this Article?

Mohammed Goryawala, Magno R. Guillen, Armando Barreto, Ruchir Bhatt, Seza Gulec, Tushar Barot, Rekha Suthar, Anthony Mcgoron, Malek Adjouadi (2011). Design and Evaluation of Parallel Processing Techniques for 3D Liver Segmentation and Volume Rendering. i-manager’s Journal on Software Engineering, 5(4), 12-27. https://doi.org/10.26634/jse.5.4.1444

References

[1]. Bister, M., Yap, C., Ng, K., & CH., T. (2007). Increasing the speed of medical image processing in MatLab®. Biomed Imaging Interv J, 3(1), e9.
[2]. Bliss, N.T., & Kepner, J. (2007). pMATLAB parallel MATLAB library. International Journal of High Performance Computing Applications, 21(3), 336-359. doi: Doi 10.1177/1094342007078446.
[3]. Chakravarti, A., Grad-Freilich, S., Laure, E., Jouvin, M., Philippon, G., Loomis, C., & Floros, E. (2008). Enhancing e-Infrastructures with Advanced Technical Computing: Parallel MATLAB® on the Grid. Paper presented at the Parallel MATLAB® on the Grid. EGEETransactions.
[4]. Chan, T. E., Sandberg, B. Y., & Vese, L. A. (2000). Active contours without edges for vector-valued images. Journal of Visual Communication and Image Representation, 11(2), 130-141.
[5]. Choy, R., & Edelman, A. (2005). Parallel MATLAB: Doing it right. Proceedings of the IEEE, 93(2), 331-341. doi: Doi 10.1109/Jproc.2004.840490.
[6]. Choy, R., Edelman, A., Gilbert, J. R., Shah, V., & Cheng, D. (2004). Star-P: High productivity parallel computing. Paper presented at the Eighth Annual Workshop on High-Performance Embedded Computing (HPEC 04).
[7]. Edelman, A. (2007). The Star-P High Performance Computing Platform. Paper presented at the IEEE International Conference on Acoustics, Speech and Signal Processing, 2007., Honolulu, HI.
[8]. Hill, M., & Marty, M. (2008). Amdahl's Law in the Multicore Era. Computer, 41(7), 33.
[9]. Hudak, D.E., Ludban, N., Krishnamurthy, A., Gadepally, V., Samsi, S., & Nehrbass, J. (2009). A computational science IDE for HPC systems: design and applications. Int. J. Parallel Program., 37(1), 91-105. doi: 10.1007/s10766-008-0084-3.
[10]. Kanungo, T., Mount, D. M., Netanyahu, N. S., Piatko, C. D., Silverman, R., & Wu, A. Y. (2002). An efficient km eans clustering algorithm : Analysis and implementation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 881-892.
[11]. Krishnamurthy, A., Nehrbass, J., Chaves, J.C., & Samsi, S. (2007). Survey of Parallel MATLAB Techniques and Applications to Signal and Image Processing. Paper presented at the IEEE International Conference on Acoustics, Speech and Signal Processing.
[12]. Lankton, S., & Tannenbaum, A. (2008). Localizing Region-Based Active Contours. IEEE Transactions on Image Processing, 17(11), 2029-2039. doi: Doi 10.1109/Tip.2008.2004611.
[13]. Mathworks, I. (2009a). Distributed Computing Toolbox, Users Guide. Paper presented at the The MathWorks, Inc.,: Natick, MA,.
[14]. Mathworks, I. (2009b). MATLAB Distributed Computing Engine. Paper presented at the MathWorks, Inc.,: Natick, MA,.
[15]. Mathworks, I. (2009c). Parallel Computing Toolbox, Users Guide. Paper presented at the The MathWorks, Inc.,: Natick, MA,.
[16]. Mirman, I. (2006). Going Parallel the New Way Desktop Engineering, 11(10), 24-25.
[17]. Pappas, T.N. (1992). An Adaptive Clustering- Algorithm for Image Segmentation. IEEE Transactions on Signal Processing, 40(4), 901-914.
[18]. Smith, D. (2009). The MathWorks Introduces New Versions of MATLAB Parallel Computing Products. The Mathworks Inc., DOI: http://www.mathworks.com/ company/pressroom/articles/article34060.html.
[19]. Trefethen, A., Menon, V., Chang, C., Czajkowski, G., Myers, C., & Trefethen, L. (1996). MultiMATLAB: MATLAB on Multiple Processors. Cornell Theory Center Technical Reports, 96-239.
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.