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بازیابی تصاویر: کاربرد در پزشکی

نوع مقاله : مقاله مروری نظام مند

نویسندگان

1 استادیار، کتابداری و اطلاع‌رسانی پزشکی، دانشگاه علوم پزشکی کرمان، کرمان، ایران

2 کارشناس ارشد، کتابداری و اطلاع‌رسانی پزشکی، دانشگاه علوم پزشکی کرمان، کرمان، ایران

3 استادیار، مدارک پزشکی و فناوری اطلاعات سلامت، دانشگاه علوم پزشکی کرمان، کرمان، ایران

چکیده

هدف هر نظام بازیابی اطلاعات پزشکی، ارائه به‌موقع اطلاعات مرتبط در زمان مناسب به کاربر مناسب است. تصاویر به‌عنوان شکلی از مدارک که می‌توانند حجم قابل توجهی از اطلاعات را منتقل کنند از اهمیت خاصی برخوردارند. در پزشکی مهمترین استفاده از تصاویر در آموزش، پژوهش و تشخیص طبی است. این استفاده گسترده نشان دهنده‌‌ی اهمیت روزافزون تصویربرداری در حیطه‌های مختلف پزشکی است. بنابراین پیشرفت‌های جدید در فنون تصویربرداری پزشکی و استفاده گسترده از آنها، برای نمونه در سیستم‌های پشتیبان تصمیم‌گیری و پزشکی مبتنی بر شواهد، اهمیت بالای بازیابی تصاویر پزشکی را نشان می‌دهد. این تصاویر برای اینکه مورد استفاده قرار گیرند باید به نحو مناسب ذخیره شوند تا در موقع نیاز بازیابی گردند. در این مقاله دو روش عمده یعنی شیوه مبتنی بر متن و شیوه مبتنی بر محتوا توصیف گردید. همچنین کاربرد سیستم‌های بازیابی تصاویر در پزشکی شرح داده شد و نمونه‌هایی از سیستم‌های موجود توصیف شدند. در نهایت اگر چه تصاویر بعضی از ویژگی‌های متفاوت از متن دارند، اما به برخی تکنیک‌ها در بازیابی متن، از جمله هوش مصنوعی و بازخورد ربط که می‌توانند جهت ارتقای سیستم‌های بازیابی تصاویر استفاده شوند، اشاره شد. واژه‌های کلیدی: ذخیره و بازیابی اطلاعات پزشکی؛ تصاویر پزشکی؛ نظام‌های بازیابی اطلاعات

کلیدواژه‌ها

عنوان مقاله [English]

Image Retrieval: Application in Medicine

نویسندگان [English]

  • Maryam Okhovati 1
  • Reza Akbarnejad 2
  • Kambiz Bahaadinbeigy 3

1 Assistant Professor, Medical Library and Information Sciences, Kerman University of Medical Sciences, Kerman, Iran

2 Medical Library and Information Sciences, Kerman University of Medical Sciences, Kerman, Iran

3 Assistant Professor, Medical Records and Health Information Technology, Kerman University of Medical Sciences, Kerman, Iran

چکیده [English]

The aim of each information retrieval is to present relevant information to the right user at the right time. Images as a kind of information can convey a large volume of information. In medicine, the most common use of images is in education, research and medical diagnosis. This wide variety of usage refers to the uprising importance of imaging through various fields of medicine. Therefore, current advances in medical imaging techniques and frequent use for example, in decision making systems and evidence-based medicine depicts the high necessity of medical images retrieval. This paper introduced text-based and content-based image retrieval systems, the application of the image systems especially in medicine. Some existing systems are described. Finally it was suggested although images have some features different from texts but some techniques in text retrieval such as artificial intelligence and relevance feedback can be used to improve the image retrieval systems. Keywords: Medical Information Storage and Retrieval; Medical Illustration; Information Retrieval Systems

کلیدواژه‌ها [English]

  • Medical Information Storage and Retrieval
  • Medical Illustration
  • Information Retrieval Systems
مراجع
  1. Kherfi ML, Ziou D, Bernardi A. Image retrieval from the world wide web: Issues, techniques, and systems. ACM Computing Surveys (CSUR). 2004;36(1):35-67.
  2. Eakins JP, Graham ME. Content-Based Image Retrieval: A report to the JISC Technology Applications Programme, January 1999. Inst for Image Data Research, Univ of Northumbria at Newcastle. 1999.
  3. Venters CC, Cooper M. A review of content-based image retrieval systems. University of Manchester, JISC Technology Applications Program (JTAP) report. 2000;1(07):00.
  4. Goodrum AA. Image information retrieval: An overview of current research. Informing Science. 2000;3(2):63-6.
  5. Müller H, Müller W, Squire DMG, Marchand-Maillet S, Pun T. Performance evaluation in content-based image retrieval: Overview and proposals. Pattern Recognition Letters. 2001;22(5):593-601.
  6. Vailaya A, Figueiredo MAT, Jain AK, Zhang HJ. Image classification for content-based indexing. Image Processing, IEEE Transactions on. 2001;10(1):117-30.
  7. Long F, Zhang H, Feng DD. Fundamentals of content-based image retrieval. Multimedia Information Retrieval and Management. 2003;4.
  8. Liu Y, Zhang D, Lu G, Ma WY. A survey of content-based image retrieval with high-level semantics. Pattern Recognition. 2007;40(1):262-82.
  9. Swain MJ, Ballard DH. Color indexing. International journal of computer vision. 1991;7(1):11-32.
  10. Ioka M. A method of defining the similarity of images on the basis of color information: IBM Research, Tokyo Research Laboratory; 1989.
  11. Stricker M, Orengo M, editors. Similarity of color images1995: Citeseer.
  12. Smith JR, Chang SF, editors. Single color extraction and image query1995: IEEE.
  13. Smith JR, Chang SF. Tools and techniques for color image retrieval. Storage & Retrieval for Image and Video Databases IV. 1996;2670:426-37.
  14. Chua TS, Tan KL, Ooi BC, editors. Fast signature-based color-spatial image retrieval1997: IEEE.
  15. Faloutsos C, Barber R, Flickner M, Hafner J, Niblack W, Petkovic D, et al. Efficient and effective querying by image content. Journal of intelligent information systems. 1994;3(3):231-62.
  16. Lu H, Ooi BC, Tan KL. Efficient image retrieval by color contents. Applications of Databases. 1994:95-108.
  17. Pass G, Zabih R, Miller J, editors. Comparing images using color coherence vectors1997: ACM.
  18. Haralick RM, Shanmugam K, Dinstein IH. Textural features for image classification. Systems, Man and Cybernetics, IEEE Transactions on. 1973;3(6):610-21.
  19. Tamura H, Mori S, Yamawaki T. Textural features corresponding to visual perception. Systems, Man and Cybernetics, IEEE Transactions on. 1978;8(6):460-73.
  20. Liu F, Picard RW. Periodicity, directionality, and randomness: Wold features for image modeling and retrieval. Pattern Analysis and Machine Intelligence, IEEE Transactions on. 1996;18(7):722-33.
  21. Kundu A, Chen JL. Texture classification using QMF bank-based subband decomposition. CVGIP: Graphical models and image processing. 1992;54(5):369-84.
  22. Manjunath BS, Ma WY. Texture features for browsing and retrieval of image data. Pattern Analysis and Machine Intelligence, IEEE Transactions on. 1996;18(8):837-42.
  23. Kaplan LM, Murenzi R, Namuduri KR, editors. Fast texture database retrieval using extended fractal features1997.
  24. Cross GR, Jain AK. Markov random field texture models. Pattern Analysis and Machine Intelligence, IEEE Transactions on. 1983(1):25-39.
  25. Ma WY, Manjunath B, editors. A comparison of wavelet transform features for texture image annotation1995: IEEE.
  26. Ohanian PP, Dubes RC. Performance evaluation for four classes of textural features. Pattern Recognition. 1992;25(8):819-33.
  27. Pentland AP. Fractal-based description of natural scenes. Pattern Analysis and Machine Intelligence, IEEE Transactions on. 1984(6):661-74.
  28. Weszka JS, Dyer CR, Rosenfeld A. A comparative study of texture measures for terrain classification. Systems, Man and Cybernetics, IEEE Transactions on. 1976(4):269-85.
  29. Rui Y, Huang TS, Chang SF. Image retrieval: Current techniques, promising directions, and open issues. Journal of visual communication and image representation. 1999;10(1):39-62.
  30. Faloutsos C, Taubin G, editors. The QBIC project: Querying images by content using color, texture, and shape1993.
  31. Hu MK. Visual pattern recognition by moment invariants. Information Theory, IRE Transactions on. 1962;8(2):179-87.
  32. Kapur D, Lakshman Y, Saxena T, editors. Computing invariants using elimination methods1995: IEEE.
  33. Rui Y, She AC, Huang TS, editors. Modified Fourier descriptors for shape representation-a practical approach1996.
  34. Zahn CT, Roskies RZ. Fourier descriptors for plane closed curves. Computers, IEEE Transactions on. 1972;100(3):269-81.
  35. Mehrotra R, Gary JE. Similar-shape retrieval in shape data management. Computer. 1995;28(9):57-62.
  36. Pentland A, Picard RW, Sclaroff S. Photobook: Content-based manipulation of image databases. International journal of computer vision. 1996;18(3):233-54.
  37. Kimia BB, Chan J, Bertrand D, Coe S, Roadhouse Z, Tek H, editors. Shock-based approach for indexing of image databases using shape1997.
  38. Tirthapura S, Sharvit D, Klein P, Kimia BB, editors. Indexing based on edit-distance matching of shape graphs1998.
  39. Arkin EM, Chew LP, Huttenlocher DP, Kedem K, Mitchell JSB, editors. An efficiently computable metric for comparing polygonal shapes1990: Society for Industrial and Applied Mathematics.
  40. Chuang GCH, Kuo CCJ. Wavelet descriptor of planar curves: Theory and applications. Image Processing, IEEE Transactions on. 1996;5(1):56-70.
  41. Mehtre BM, Kankanhalli MS, Lee WF. Shape measures for content based image retrieval: a comparison. Information Processing & Management. 1997;33(3):319-37.
  42. Hermes T, Klauck C, Kreyss J, Zhang J. Image retrieval for information systems: Univ. Bremen; 1995.
  43. Smeulders A, Gevers T, Geusebroek JM, Worring M, editors. Invariance in content-based retrieval2000: IEEE.
  44. Flickner M, Sawhney H, Niblack W, Ashley J, Huang Q, Dom B, et al. Query by image and video content: The QBIC system. Computer. 1995;28(9):23-32.
  45. Lee D, Barber R, Niblack W, Flickner M, Hafner J, Petkovic D, editors. Indexing for complex queries on a query-by-content image database1994: IEEE.
  46. Niblack W, Barber R, Equitz W, Flickner M, Glasman E, Petkovic D, et al. The QBIC project: Querying images by content using color, texture: and shape. Technical Report RJ 9203 (81511), IBM Research Division1993.
  47. Bach J, Fuller C, Gupta A, Hampapur A, Horowitz B, Humphrey R, et al., editors. fe.(1996). The Virage image search engine: An open framework for image management.
  48. Gupta A, Jain R. Visual information retrieval. Communications of the ACM. 1997;40(5):70-9.
  49. Jacobs CE, Finkelstein A, Salesin DH, editors. Fast multiresolution image querying1995: ACM.
  50. Carson C, Belongie S, Greenspan H, Malik J. Blobworld: Image segmentation using expectation-maximization and its application to image querying. Pattern Analysis and Machine Intelligence, IEEE Transactions on. 2002;24(8):1026-38.
  51. Rummukainen M, Laaksonen J, Koskela M. An efficiency comparison of two content-based image retrieval systems, GIFT and PicSOM. Image and Video Retrieval. 2003:500-10.
  52. Shyu CR, Brodley CE, Kak AC, Kosaka A, Aisen AM, Broderick LS. ASSERT: a physician-in-the-loop content-based retrieval system for HRCT image databases. Computer Vision and Image Understanding. 1999;75(1):111-32.
  53. Lehmann TM, Gold M, Thies C, Fischer B, Spitzer K, Keysers D, et al. Content-based image retrieval in medical applications. Methods of Information in Medicine. 2004;43(4):354-61.
  54. Antani S, Lee D, Long LR, Thoma GR. Evaluation of shape similarity measurement methods for spine X-ray images. Journal of Visual Communication and Image Representation. 2004;15(3):285-302.
  55. Dy JG, Brodley CE, Kak A, Broderick LS, Aisen AM. Unsupervised feature selection applied to content-based retrieval of lung images. Pattern Analysis and Machine Intelligence, IEEE Transactions on. 2003;25(3):373-8.
  56. Korn P, Sidiropoulos N, Faloutsos C, Siegel E, Protopapas Z. Fast and effective retrieval of medical tumor shapes. Knowledge and Data Engineering, IEEE Transactions on. 1998;10(6):889-904.
  57. Yu SN, Chiang CT, Hsieh CC. A three-object model for the similarity searches of chest CT images. Computerized Medical Imaging and Graphics. 2005;29(8):617-30.
  58. Oliveira LLG, Ribeiro LHV, de Oliveira RM, Coelho CJ, S Andrade ALS. Computer-aided diagnosis in chest radiography for detection of childhood pneumonia. International journal of medical informatics. 2008;77(8):555-64.
  59. Xu X, Lee DJ, Antani S, Long LR. A spine X-ray image retrieval system using partial shape matching. Information Technology in Biomedicine, IEEE Transactions on. 2008;12(1):100-8.
  60. Nomir O, Abdel-Mottaleb M. Hierarchical contour matching for dental X-ray radiographs. Pattern Recognition. 2008;41(1):130-8.
  61. Greenspan H, Pinhas AT. Medical image categorization and retrieval for PACS using the GMM-KL framework. Information Technology in Biomedicine, IEEE Transactions on. 2007;11(2):190-202.
  62. Rahman MM, Bhattacharya P, Desai BC. A framework for medical image retrieval using machine learning and statistical similarity matching techniques with relevance feedback. Information Technology in Biomedicine, IEEE Transactions on. 2007;11(1):58-69.
  63. Rahman MM, Desai BC, Bhattacharya P. Medical image retrieval with probabilistic multi-class support vector machine classifiers and adaptive similarity fusion. Computerized Medical Imaging and Graphics. 2008;32(2):95-108.
  64. Yao J, Zhang ZM, Antani S, Long R, Thoma G. Automatic medical image annotation and retrieval. Neurocomputing. 2008;71(10):2012-22.
  65. Chu WW, Hsu CC, Cardenas AF, Taira RK. Knowledge-based image retrieval with spatial and temporal constructs. Knowledge and Data Engineering, IEEE Transactions on. 1998;10(6):872-88.
  66. Minka TP, Picard R. An image database browser that learns from user interaction: Citeseer; 1996.
  67. Chang E, Wang J, Li C, Wiederhold G. RIME: A replicated image detector for the world-wide web. 1998.
  68. Azad A., Okhovati M. Intelligent Systems and their Applications in Library & Information Science. Library and Information Science, Quarterly Journal of Central Library and Documentation Centre of Astan Quds Razavi. Vol.24, No. 4. [Article in Persian]
  69. Ebrahimi, N. Illustration of Books for Children. [1st Edition]. Tehran: Aghah Publications, autumn 1367. [Book in Persian]
  70. ML Pao. Concepts of Information Retrieval. Translated by Azad, A and Fattahi, R. Mashhad: Ferdowsi University of Mashhad, Institute Press, 1378. [Book Translated in Persian]
  71. Saryazdi et al. A review of existing methods in Image Retrieval and Introducing of Image Databases. 1st Technical Report of " Provide new methods to the multi-modal retrieval of color images "Research Project., Project code 8732316. Iran Telecommunication Research Center. [Technical Report in Persian]
  72. Nezamabadi- pour, H. Application-dependent features in an image, color image retrieval. Ph.D. dissertation, Tarbiat Moddares University, Department of Electrical Engineering, Summer 1383. [dissertation in Persian]
  73. Okhovati, M. The Concept of Relevance in Information Retrieval Systems: A Review of Existing Theory and literature. Informology. Vol 5. NO.1. 1383. [Article in Persian]
  74. Tang LHY, Hanka R, Ip HHS. A review of intelligent content-based indexing and browsing of medical images. Health Informatics Journal. 1999;5(1):40-9.
  75. Kambiz Bahaadinbeigy and Kanagasingam Yogesan (2011). Advances in Teleophthalmology: Summarising Published Papers on Teleophthalmology Projects, ISBN: 978-953-307-161-9, InTech, Available from:
  76. http://www.intechopen.com/books/advances-in-telemedicine-applications-in-various-medical-disciplines-and-geographical-regions/advances-in-teleophthalmology-summarising-published-papers-on-teleophthalmology-projects
  77. Kambiz Bahaadinbeigy and Kanagasingam Yogesan (2012). A Literature Review of Teleophthalmology Projects from Around the Globe, ISBN: 978-3-642-25809-1,Springer,Available from:
  78. https://springerlink3.metapress.com/content/h471074184283767/resource-secured/?target=fulltext.pdf&sid=pp50smyxlmrn1nnnuctsrof4&sh=www.springerlink.com
مدیریت اطلاعات سلامت
دوره 10، شماره 4 - شماره پیاپی 4
آبان 1392
صفحه 627-644
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