APPLICATION OF PARALLEL PROCESSING - A CASE STUDY ON VERTEBRA DETECTION IN X-RAY IMAGES Mohamad Khairul Hazman bin Mohamad Zaidi #1, Mohamed Faidz Mohamed Said #2 # Faculty of Computer & Mathematical Sciences, Universiti Teknologi MARA 70300 Seremban, Negeri Sembilan, MALAYSIA 1 khairul.hazman.94@gmail.com 2 faidzms@ieee.org Abstract— X-ray computed tomography (CT) is non-invasive imaging technique that creates cross-sectional images of an object from two-dimensional X-ray projections. For the case study on the vertebra detection in X-ray images, this technology is linked to the machine called the Computed Axial Tomography or well known as the CAT or CT scan. This application on the parallel processing is widely used in the medical field society to detect the vertebra in the X-ray images. It is a present-day imaging instrument that consolidates X-rays with PC innovation to deliver a more point by point, cross-sectional picture of your body. A CT scan gives your specialist a chance to see the size, shape, and position of structures that are somewhere inside your body, for example, organs, tissues, tumors and vertebrae. It also gives a much detail look on the problems parts of the body that need to be cured. Beside vertebrae detection, it is also used to detect cancer cells. CT scan have helped us a lot in the medical world by detecting what our plain eyes cannot see. Keyword: vertebra, tumors, parallel processing, cancer REFERENCES [1] CT scan. Available: https://en.wikipedia.org/wiki/CT_scan, access 21 June 2017. [2] J. Deng, "Parallel computing techniques for computed tomography," 2011. [3] F. Lecron, S. A. Mahmoudi, M. Benjelloun, S. Mahmoudi, and P. Manneback, "Heterogeneous computing for vertebra detection and segmentation in X-ray images," Journal of Biomedical Imaging, vol. 2011, p. 5, 2011. [4] S. A. Mahmoudi, F. Lecron, P. Manneback, M. Benjelloun, and S. Mahmoudi, "GPU-based segmentation of cervical vertebra in X-ray images," in Cluster Computing Workshops and Posters (CLUSTER WORKSHOPS), 2010 IEEE International Conference on, 2010, pp. 1-8. [5] A. Bilgot, O. Le Cadet, V. Perrier, and L. Desbat, "Edge detection and classification in X-Ray images. Application to interventional 3D vertebra shape reconstruction," proceedings de Surgetica, 2005. [6] Y. M. Kadah, K. Z. Abd-Elmoniem, and A. A. Farag, "Parallel computation in medical imaging applications," International journal of biomedical imaging, vol. 2011, 2012. [7] J. Ni, X. Li, T. He, and G. Wang, "Review of parallel computing techniques for computed tomography image reconstruction," Current Medical Imaging Reviews, vol. 2, pp. 405-414, 2006. [8] A. Miller, B. Blott, and T. Hames, "Review of neural network applications in medical imaging and signal processing," Medical and Biological Engineering and Computing, vol. 30, pp. 449-464, 1992. [9] H. Luo, X. Wang, and D. Foos, "Processing and measuring the spine in radiographs," ed: Google Patents, 2006. [10] T. M. Lehmann, B. B. Wein, J. Dahmen, J. Bredno, F. Vogelsang, and M. Kohnen, "Content-based image retrieval in medical applications: a novel multistep approach," in Electronic Imaging, 1999, pp. 312-320. [11] W. A. Kalender, Computed tomography: fundamentals, system technology, image quality, applications: John Wiley & Sons, 2011. [12] L. W. Goldman, "Principles of CT and CT technology," Journal of nuclear medicine technology, vol. 35, pp. 115-128, 2007. [13] J. Giersch, A. Weidemann, and G. Anton, "ROSI—An object-oriented and parallel-computing Monte Carlo simulation for X-ray imaging," Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 509, pp. 151-156, 2003. [14] Y. Censor, "Parallel application of block-iterative methods in medical imaging and radiation therapy," Mathematical Programming, vol. 42, pp. 307-325, 1988. [15] T. J. Bruijns, P. L. Alving, E. L. Baker, R. F. Bury, A. R. Cowen, N. Jung, et al., "Technical and clinical results of an experimental flat dynamic (digital) x-ray image detector (FDXD) system with real-time corrections," in Medical Imaging'98, 1998, pp. 33-44. [16] M. F. M. Said, M. N. Taib, and S. Yahya, "Analysis of the CPU Utilization for Point-to-Point Communication Operations in a Beowulf Cluster System," in 2008 International Symposium on Information Technology, 2008, pp. 1-6. [17] M. F. M. Said, M. N. Taib, and S. Yahya, "Analysis of TCP/IP Overhead on Overlapping Message Transfer and Computation in a Distributed Memory System Architecture," International Journal of Advanced Research in Computer Science (IJARCS), vol. 3, pp. 22-36, 2012. [18] M. F. M. Said, S. Yahya, and M. N. Taib, "Analysis of Different Programming Primitives used in a Beowulf Cluster," International Journal of Computer and Information Technology (IJCIT), vol. 1, pp. 25-33, 2012.