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Compression of image sequences in interactive medical teleconsultations

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Języki publikacji
EN
Abstrakty
EN
Interactive medical teleconsultations are an important tool in modern medical practice. Their applications include remote diagnostics, conferences, work- shops, and classes for students. In many cases, standard medium or low-end machines are employed, and the teleconsultation systems must be able to provide a high quality of user experience with very limited resources. Particularly problematic are large datasets consisting of image sequences that need to be accessed fluently. The main issue is insufficient internal memory; therefore, proper compression methods are crucial. However, a scenario where image sequences are kept in a compressed format in the internal memory and decompressed on- the-fly when displayed is difficult to implement due to performance issues. In this paper, we present methods for both lossy and lossless compression of medical image sequences that only require compatibility with the Pixel Shader 2.0 standard, which is present even on relatively old, low-end devices. Based on the evaluation of quality, size reduction, and performance, these methods have been proven to be suitable and beneficial for medical teleconsultation applications
Wydawca
Czasopismo
Rocznik
Strony
95--114
Opis fizyczny
Bibliogr. 32 poz., rys., wykr., tab.
Twórcy
autor
  • AGH University of Science and Technology, Department of Computer Science, Krakow, Poland
  • AGH University of Science and Technology, Department of Computer Science, Krakow, Poland
Bibliografia
  • [1] Bairagi V.K.: Symmetry-Based Biomedical Image Compression. Journal of Digital Imaging, vol. 28(6), pp. 718–726, 2015, http://link.springer.com/10. 1007/s10278-015-9779-3 .
  • [2] Chłopkowski M., Walkowiak R.: A general purpose lossless data compression method for GPU. Journal of Parallel and Distributed Computing, vol. 75, pp. 40– 52, 2015, http://dx.doi.org/10.1016/j.jpdc.2014.09.016 .
  • [3] Czekierda L., Malawski F., Wyszkowski P.: Holistic approach to design and implementation of a medical teleconsultation workspace. Journal of Biomedical Informatics, vol. 57, pp. 225–244, 2015.
  • [4] Czekierda L., Masternak T., Zieliński K.: Evolutionary approach to development of collaborative teleconsultation system for imaging medicine. IEEE transactions on information technology in biomedicine: a publication of the IEEE Engineering in Medicine and Biology Society, vol. 16(4), pp. 550–60, 2012, http://www.ncbi. nlm.nih.gov/pubmed/22510953 .
  • [5] Franco J., Bernabe G., Fernandez J., Ujaldon M.: Parallel 3D fast wavelet transform on manycore GPUs and multicore CPUs. Procedia Computer Science, vol. 1(1), pp. 1101–1110, 2010.
  • [6] Gennari J.H., Weng C., Benedetti J., McDonald D.W.: Asynchronous communication among clinical researchers: a study for systems design. International Journal of Medical Informatics, vol. 74(10), pp. 797–807, 2005, http: //www.ncbi.nlm.nih.gov/pubmed/16023408 .
  • [7] Hosseini S.M., Naghsh-Nilchi A.R.: Medical ultrasound image compression using contextual vector quantization. Computers in Biology and Medicine, vol. 42(7), pp. 743–50, 2012, http://www.ncbi.nlm.nih.gov/pubmed/22608347 .
  • [8] Hsu Y.C., Lin P.Y., Hsu S.J., Chan C.T.: Design and Implementation of Teleconsultation System for Instant Treatment. 2008 2nd International Conference on Bioinformatics and Biomedical Engineering, pp. 1355–1358, 2008, http: //ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4535548 .
  • [9] ICS-AGH: TeleDICOM. http://www.teledicom.pl/index.php/en/ .
  • [10] ISO/IEC-15948:2004: Information technology – Computer graphics and image processing – Portable Network Graphics (PNG): Functional specification. http: //www.iso.org/iso/catalogue_detail.htm?csnumber=29581 .
  • [11] ITU: JPEG Standard – JPEG ISO/IEC 10918-1 ITU-T Recommendation T.81. http://www.w3.org/Graphics/JPEG/itu-t81.pdf.
  • [12] ITU: Recommendation T. 45: Run-length Colour Encoding. http://www.itu. int/rec/T-REC-T.45-200002-I/en .
  • [13] ITU: T.87 Information technology – Lossless and near-lossless compression of continuous-tone still images – Baseline. http://www.itu.int/rec/T-REC-T.87/ en
  • [14] Joint-Photographic-Experts-Group: JPEG2000. https://jpeg.org/jpeg2000/ index.html .
  • [15] Khronos-Group: OpenCL. https://www.khronos.org/opencl/ .
  • [16] Lasierra N., Alesanco A., Gilaberte Y., Magall ́on R., Garc ́ıa J.: Lessons learned after a three-year store and forward teledermatology experience using internet: Strengths and limitations. International Journal of Medical Informatics , vol. 81(5), pp. 332–43, 2012, http://www.ncbi.nlm.nih.gov/pubmed/22425394 .
  • [17] Lee J.S., Tsai C.T., Pen C.H., Lu H.C.: A real time collaboration system for teleradiology consultation. International Journal of Medical Informatics, vol. 72(1-3), pp. 73–79, 2003, http://linkinghub.elsevier.com/retrieve/ pii/S1386505603001308 .
  • [18] Lee J.W., Kim B., Yoon K.S.: CUDA-based JPEG2000 encoding scheme. 16th International Conference on Advanced Communication Technology, pp. 671–674, 2014, http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber= 6779047 .
  • [19] Luccichenti G., Cademartiri F., Pichiecchio A., Bontempi E., Sabatini U., Bastianello S.: User interface of a teleradiology system for the MR assessment of multiple sclerosis. Journal of Digital Imaging, vol. 23(5), pp. 632– 8, 2010, http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid= 3046674{\&}tool=pmcentrez{\&}rendertype=abstract .
  • [20] Miaou S.G., Ke F.S., Chen S.C.: A lossless compression method for medical image sequences using JPEG-LS and interframe coding. IEEE transactions on information technology in biomedicine: a publication of the IEEE Engineering in Medicine and Biology Society, vol. 13(5), pp. 818–21, 2009, http://www.ncbi. nlm.nih.gov/pubmed/19447732 .
  • [21] MSDN: DXT1 texture format. https://msdn.microsoft.com/en-us/library/ windows/desktop/bb147243(v=vs.85).aspx .
  • [22] MSDN: Windows Presentation Foundation (WPF). https://msdn.microsoft. com/en-us/library/aa663364.aspx
  • [23] NEMA: DICOM standard. http://dicom.nema.org/standard.html
  • [24] NVIDIA: CUDA. http://www.nvidia.com/object/cuda_home_new.html
  • [25] OsiriX: DICOM sample image sets. http://www.osirix-viewer.com/ datasets/
  • [26] Ozsoy A., Swany M., Chauhan A.: Optimizing LZSS compression on GPGPUs. Future Generation Computer Systems , vol. 30(1), pp. 170–178, 2014, http://dx. doi.org/10.1016/j.future.2013.06.022
  • [27] Pizzolante R., Castiglione A., Carpentieri B., Santis A.D.: Parallel low- complexity lossless coding of three-dimensional medical images. Proceedings – 2014 International Conference on Network-Based Information Systems, NBiS 2014 , pp. 91–98, 2014.
  • [28] Sanchez V., Abugharbieh R., Nasiopoulos P.: Symmetry-based scalable lossless compression of 3D medical image data. IEEE Transactions on Medical Imaging, vol. 28(7), pp. 1062–1072, 2009.
  • [29] Sriraam N., Shyamsunder R.: 3-D medical image compression using 3-D wavelet coders. Digital Signal Processing , vol. 21(1), pp. 100–109, 2011, http: //linkinghub.elsevier.com/retrieve/pii/S1051200410001442
  • [30] Wang B., Govindan P., Gonnot T., Saniie J.: Acceleration of ultrasonic data compression using OpenCL on GPU. In: 2015 IEEE International Conference on Electro/Information Technology (EIT), pp. 305–309, 2015.
  • [31] Weinlich A., Rehm J., Amon P., Hutter A., Kaup A.: Massively parallel lossless compression of medical images using least-squares prediction and arithmetic coding. 2013 IEEE International Conference on Image Processing , pp. 1680–1684, 2013, http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber= 6738346 .
  • [32] Wu Y.G., Tai S.C.: Medical image compression by discrete cosine transform spectral similarity strategy. IEEE Transactions on Information Technology in Biomedicine, vol. 5(3), pp. 236–243, 2001
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-8159377f-9643-4eb9-ba41-123afe2049da
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