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Middlesex Medical Image Repository Dr. Yu Qian y.qian@mdx.ac.uk

Middlesex Medical Image Repository Dr. Yu Qian y.qian@mdx.ac.uk. Content. Introduction of MIRAGE project Introduction of Content-based Image Retrieval(CBIR) Proposed framework for MIRAGE CBIR for 2D medical images CBIR for 3D medical images Image labelling

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Middlesex Medical Image Repository Dr. Yu Qian y.qian@mdx.ac.uk

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  1. Middlesex Medical Image RepositoryDr. Yu Qian y.qian@mdx.ac.uk

  2. Content • Introduction of MIRAGE project • Introduction of Content-based Image Retrieval(CBIR) • Proposed framework for MIRAGE • CBIR for 2D medical images • CBIR for 3D medical images • Image labelling • what we have done and future work

  3. PART IIntroduction of MIRAGE Project

  4. MIRAGE(Middlesex medical Image Repository with a CBIR ArchivinGEnvironment) • Aim: To develop a repository of medical images benefiting MSc and research students in the immediate term and serve a wider community in the long term in providing a rich supply of medical images for data mining, to complement MU current online e-learning system, OASIS+. • Collaboration between three parties at MU, including EIS,CLQE and CIE. • http://image.mdx.ac.uk/ JISC • Innovation in the use of ICT for education and research. • http://www.jisc.ac.uk/

  5. PART IIContent-Based Image Retrieval(CBIR)

  6. Content-Based Image Retrieval(CBIR) • CBIR can index an image using visual contents that an image is carrying, such as colour, texture, shape and location. • Query by Example (QBE) • Query by Feature (QBF) • Query by Sketch(QBS) • For example:

  7. Colour-Based Retrieval

  8. Texture-Based Retrieval

  9. Shape-Based Retrieval

  10. Query by Feature

  11. Query by Sketch

  12. Framework of Content-Based Image Retrieval

  13. Compared with Text-Based Image Retrieval(TBIR)

  14. CBIR for Medical Images The need for CBIR • For clinical diagnoses • For teaching and research CBIRS • ASSERT(HRCT Lung) • FICBDS(PET) • CBIRS(Spine X-Ray) • BASS (Breast Cancer)

  15. PART IIIFramework for MIRAGE

  16. Proposed Framework for MRIAGE

  17. 1) CBIR for 2D Medical Image ----GIFT

  18. GIFT(GNU Image Finding Tool) GIFT is open framework for content-based image retrieval and is developed by University of Geneva. • Query by example and multiple query • Relevance Feedback • Distributed architecture (Client - Server) • MRML---C-S communication protocol Demo:

  19. GIFT Framework

  20. 2) CBIR for 3D Medical Images

  21. Proposed Framework for 3D Image Retrieval

  22. 3D Texture Feature Extraction • 3D Grey Level Co-occurrence Matrices (3D GLCM) • 3D Wavelet Transform (3D WT) • 3D Gabor Transform (3D GT) • 3D Local Binary Pattern (3D LBP)

  23. Similarity Measurement • Histogram Intersection(3D LBP) • Normalized Euclidean distance (3D GLCM,3D WT,3D GT)

  24. Experiment Results

  25. Processing and Query time

  26. 3) Image Labelling

  27. Image labelling

  28. PART IVWhat We Have Done and Future Work

  29. What We Have Done • GIFT framework Uploaded and processed 73000 images • 3D image retrieval Created 3D feature database using four 3D feature descriptors (One paper had been published in IADIS e-health 2010). • Link MIRAGE to OASIS+

  30. Future Work • Continue working on image labelling • Plug 3D image retrieval into GIFT framework • System evaluation • Final report

  31. Question?Thanks

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