Publication: Implementation of a low-cost mobile devices to support medical diagnosis
Loading...
Official URL
Full text at PDC
Publication Date
2013
Advisors (or tutors)
Editors
Journal Title
Journal ISSN
Volume Title
Publisher
Hindawi Publishing Corporation
Citations
Exportar
Abstract
Medical imaging has become an absolutely essential diagnostic tool for clinical practices; at present, pathologies can be detected with an earliness never before known. Its use has not only been relegated to the field of radiology but also, increasingly, to computer-based imaging processes prior to surgery. Motion analysis, in particular, plays an important role in analyzing activities or behaviors of live objects in medicine. This short paper presents several low-cost hardware implementation approaches for the new generation of tablets and/or smartphones for estimating motion compensation and segmentation in medical images. These systems have been optimized for breast cancer diagnosis using magnetic resonance imaging technology with several advantages over traditional X-ray mammography, for example, obtaining patient information during a short period. This paper also addresses the challenge of offering a medical tool that runs on widespread portable devices, both on tablets and/or smartphones to aid in patient diagnostics.
Description
The authors would like to thank Dr. Thomas Schlossbauer from the Department of Clinical Radiology, University of Munich, Munich, Germany, for providing the breast MRI images used for this study. This work was partially supported by Projects MICINN TIN2008-0508 and TIN 2012-32180 (Spain).
Unesco subjects
Keywords
Citation
[1] A. M. Baese, Pattern Recognition for Medical Imaging, Elsevier, New York, NY, USA, 2004.
[2] M. Brammer, “Correspondence: brain scam?” Nature Neuroscience, vol. 7, article 1015, 2004.
[3] H. Oh and H. Lee, “Block-matching algorithm based on an adaptive reduction of the search area for motion estimation,” Real-Time Imaging, vol. 6, no. 5, pp. 407–414, 2000.
[4] C. Huang and Y. Chen, “Motion estimation method using a 3D steerable filter,” Image and Vision Computing, vol. 13, no. 1, pp. 21–32, 1995.
[5] S. Baker and I.Matthews, “Lucas-Kanade 20 years on: a unifying framework,” International Journal of Computer Vision, vol. 56, no. 3, pp. 221–255, 2004.
[6] B. McCane, K. Novins, D. Crannitch, and B. Galvin, “On benchmarking optical flow,” Computer Vision and Image Understanding, vol. 84, no. 1, pp. 126–143, 2001.
[7] H. Liu, T.-H. Hong, M. Herman, T. Camus, and R. Chellappa, “Accuracy vs efficiency trade-offs in optical flow algorithms,” Computer Vision and Image Understanding, vol. 72, no. 3, pp. 271–286, 1998.
[8] J. L. Barron, D. J. Fleet, and S. S. Beauchemin, “Performance of optical flow techniques,” International Journal of Computer Vision, vol. 12, no. 1, pp. 43–77, 1994.
[9] D. Seal, ARM Architecture Reference Manual, Addison-Wesley, Boston, Mass, USA, 2nd edition, 2001.
[10] http://www.forbes.com/sites/darcytravlos/2013/02/28/armholdings-and-qualcomm-the-winners-in-mobile/.
[11] http://seekingalpha.com/article/1300481-lilliput-slays-gulliverarm-vs-intel-and-why-intel-lost-the-war.
[12] J. Owens, “GPU architecture overview,” in Proceedings of the 34th International Conference on Computer Graphics and Interactive Techniques (SIGGRAPH ’07), San Diego, Calif, USA, August, 2007.
[13] J. Díaz, E. Ros, F. Pelayo, E. M. Ortigosa, and S. Mota, “FPGAbased real-time optical-flow system,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 16,no. 2, pp. 274–279, 2006.
[14] J. Díaz, E. Ros, R. Rodriguez-Gomez, and B. Del Pino, “Realtime architecture for robust motion estimation under varying illumination conditions,” Journal ofUniversal Computer Science, vol. 13, no. 3, pp. 363–376, 2007.
[15] B. K. P. Horn and B. G. Schunck, “Determining optical flow,” Artificial Intelligence, vol. 17, no. 1–3, pp. 185–203, 1981.
[16] L. Martín, A. Zuloaga, C. Cuadrado, J. Lázaro, and U. Bidarte, “Hardware implementation of optical flow constraint equation using FPGAs,” Computer Vision and Image Understanding, vol. 98, no. 3, pp. 462–490, 2005.
[17] C.Garcia, G. Botella, F. Ayuso, M. Prieto, and F. Tirado, “GPUbased acceleration of bioinspired motion estimation model,” Concurrency and Computation: Practice and Experience, vol. 25, no. 8, pp. 1037–1056, 2012.
[18] G. Botella, A. Garc´ıa, M. Rodriguez-Alvarez, E. Ros, U. Meyer-Bäse, and M. C. Molina, “Robust bioinspired architecture for optical-flow computation,” IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 18,no. 4, pp.616–629, 2010.
[19] C. Garcia, G Botella, F Ayuso, M. Prieto, and F. Tirado, “Multi-GPU based onmulticriteria optimization for motion estimation system,” EURASIP Journal on Advances in Signal Processing, vol. 2013, article 23, 2013.
[20] A. Johnston, P.W.McOwan, and C. P. Benton, “Robust velocity computation from a biologically motivated model of motion perception,” Proceedings of the Royal Society B, vol. 266, no. 1418, pp. 509–518, 1999.
[21] http://www.seco.com/en/newselem/carma-devkit-now-shipping.
[22] A. Johnston and C. W. G. Clifford, “A unified account of three apparent motion illusions,” Vision Research, vol. 35, no. 8, pp. 1109–1123, 1995.
[23] A. Johnston and C. W. G. Clifford, “Perceived motion of contrast-modulated gratings: Predictions of the multi-channel gradient model and the role of full-wave rectification,” Vision Research, vol. 35, no. 12, pp. 1771–1783, 1995.
[24] P. W. McOwan, C. Benton, J. Dale, and A. Johnston, “A multidifferential neuromorphic approach tomotion detection,” International Journal of Neural Systems, vol. 9, no. 5, pp. 429–434, 1999.
[25] A. Johnston, P. W. McOwan, and C. P. Benton, “Biological computation of image motion from flows over boundaries,” Journal of Physiology Paris, vol. 97, no. 2-3, pp. 325–334, 2003.
[26] H. Wallach, “On perceived identity: 1. The direction of motion of straight lines,” in On Perception, H.Wallach, Ed., Quadrangle, New York, NY, USA, 1976.
[27] B. K. P. Horn and B. G. Schunck, “Determining optical flow,” Artificial Intelligence, vol. 17, no. 1–3, pp. 185–203, 1981.
[28] E. H. Adelson and J. R. Bergen, “Spatiotemporal energy models for the perception of motion,” Journal of the Optical Society of America A, vol. 2, no. 2, pp. 284–299, 1985.
[29] E. P. Ong and M. Spann, “Robust optical flow computation based on least-median-of-squares regression,” International Journal of Computer Vision, vol. 31, no. 1, pp. 51–82, 1999.
[30] J. J. Koenderink, “Optic flow,” Vision Research, vol. 26, no. 1, pp. 161–180, 1986.
[31] K. Nakayama, “Biological image motion processing: a review,” Vision Research, vol. 25, no. 5, pp. 625–660, 1985.
[32] A. Mitiche, Y. F. Wang, and J. K. Aggarwal, “Experiments in computing optical flow with the gradient-based, multiconstraint method,” Pattern Recognition, vol. 20, no. 2, pp. 173–179, 1987.
[33] S. Baker and I.Matthews, “Lucas-Kanade 20 years on: a unifying framework,” International Journal of Computer Vision, vol. 56, no. 3, pp. 221–255, 2004.
[34] B. D. Lucas and T. Kanade, “An iterative image registration technique with an application to stereo vision,” in Proceedings of the DARPA Image Understanding Workshop, pp. 121–130, April 1981.
[35] A. Bainbridge-Smith and R. G. Lane, “Determining optical flow using a differential method,” Image and Vision Computing, vol. 15, no. 1, pp. 11–22, 1997.
[36] N. Otsu, “A Threshold selection method from gray-level histograms,” IEEE Transactions on Systems, Man and Cybernetics, vol. 9, no. 1, pp. 62–66, 1979.
[37] Z. Jun and H. Jinglu, “Image segmentation based on 2D Otsu method with histogram analysis,” in Proceedings of the International Conference on Computer Science and Software Engineering, (CSSE ’08), pp. 105–108,Wuhan, China, December 2008.
[38] http://www.seco.com.