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Dr. Yu(Jade) Qian y.qian@mdx.ac.uk

Dr. Yu(Jade) Qian y.qian@mdx.ac.uk. MIRAGE I & II. Content. Introduction of MIRAGE project Content-based 3D brain images retrieval and visualization Conclusion and future work Demonstration. PART I Introduction of MIRAGE Project.

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Dr. Yu(Jade) Qian y.qian@mdx.ac.uk

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  1. Dr. Yu(Jade) Qiany.qian@mdx.ac.uk MIRAGE I & II

  2. Content • Introduction of MIRAGE project • Content-based 3D brain images retrieval and visualization • Conclusion and future work • Demonstration

  3. PART IIntroduction of MIRAGE Project

  4. MIRAGE(Middlesex medical Image Repository with a CBIRArchivinGEnvironment) Phase 1:MIRAGE- from CreationtoArchiving • Strand : Start-up Repository funded by JISC • Between Apr. 2009- Sep. 2010. • 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+. Phase 2: MIRAGE 2011 – from Archiving to Creation • Strand : Take-up and Embedding funded by JISC • Between Feb. 2011- Oct. 2011. • Aim: To enrich the current repository MIRAGE with two necessities of ‘3D Viewer’ and ‘Uploading’ to meet users’ needs, leading to a sustainable, usable and flexible model of data management.

  5. Framework for MRIAGE I &II

  6. Phase 1: MIRAGE – Online System(1) • Server side: • Image collection: Accommodating 100,000 2D images and 100 3D images • Visual feature extraction: Pre-processing off-line using C++ and Perl. • Indexing file creation • Client side: • Interface based on PHP generate dynamic web pages. • Client – Server communication protocol: • MRML: a XML based protocol

  7. Phase 1: MIRAGE – Online System(2)Interface (1) Home page (2) Query and retrieval results

  8. Phase 2: MIRAGE2011 – Online System(1)Image Uploading MIRAGE2011 MIRAGE

  9. Phase 2: MIRAGE2011 – Online System(2)3D Viewer MIRAGE Montage MIRAGE 2011

  10. PART IIContent-based 3D Brain Images Retrieval and VisualizationY. Qian, X. Gao , M. Loomes, R. Comley, B. Barn, R. Hui, Z. Tian,Content-based Retrieval of 3D Medical Images,eTELEMED 2011, February, 2011.(Best paper award, has been invited to be extended to a journal paper).

  11. CBIR for 3D Brain Image ------Introduction 2D brain images ----- 3D Brain • Shape-based Surface of a 3D object(e.g. tumor) • Texture-based Inside of a 3D object( e.g.textures representing tissue structure properties) Aim: To develop a fast texture-based 3D brain retrieval method

  12. CBIR for 3D Brain Image ---Methodology(1)Proposed Framework

  13. CBIR for 3D Brain Image ---Methodology(2)Pre-processing 1) Spatial Normalization---Statistical Parametric Mapping (SPM5) Transform each individual brain into a standard brain template 2) Divide 3D brain into 64 non-overlapping equally sized blocks

  14. CBIR for 3D Brain Image ---Methodology(3)Extraction of Volumetric Texture Features • 3D Grey Level Co-occurrence Matrices (3D GLCM) • 3D Wavelet Transform (3D WT) • 3D Gabor Transform (3D GT) • 3D Local Binary Pattern (3D LBP)

  15. Extraction of Volumetric Textures (1) ------3D Grey Level Co-occurrence Matrices (3D GLCM) 3D GLCM is two dimensional matrices of the joint probability p(i,j) of occurrence of a pair of gray values (i,j) separated by a displacement d = (dx,dy,dz). Formula: Feature: • 52 Displacement vectors: 4 distance * 13 direction = 52 • 4 Haralick texture features: energy, entropy, contrast and homogeneity • Feature vector: 208 components (=4 (features) * 52 (matrices)). 13 directions

  16. Extraction of Volumetric Textures (2) ------ 3D Wavelet Transform (3D WT) 3D WT provides a spatial and frequency representation of a volumetric image. Two scale 3D Wavelet Transform: Feature: • Mean and Standard deviation • Feature vector: 30 components (2 (features) +15 (sub-bands))

  17. Extraction of Volumetric Textures (3) ------ 3D Gabor Transform (3D GT) A set of 3D Gabor filters: Gabor Transform: Feature: • 144 Gabor filters 4 (F) *6(θ)*6(Φ) =144 • Mean and Standard deviation • Feature vector: 288 components (2 (features) +144(filters))

  18. Extraction of Volumetric Textures (4) ------ 3D Local Binary Pattern (3D LBP) Local binary pattern(LBP) is a set of binary code Ci to define texture in a local neighborhood (p,r). A histogram Hi is then generated to calculate the occurrences of different binary patterns. LBP on three orthogonal planes (LBP-TOP), i.e., XY, XZ, and YZ planes, expressed as Feature: • 59 binary patterns • Feature vector: 177 components (=59(patterns)*3(planes)

  19. CBIR for 3D Brain Image ---Methodology(4)Retrieval ---Similarity Measurement • Histogram Intersection(3D LBP) • Normalized Euclidean distance (3D GLCM,3D WT,3D GT)

  20. CBIR for 3D Brain Image ---Methodology(5)Lesion Detection Assume bilateral symmetry of a normal brain along its mid-plane

  21. Evaluation ---- Test Dataset • 100 MR brain images • Size: 256  256  44 • DICOM (Digital Imaging and Communications in Medicine) format • Collected from Neuro-imaging Centre at Beijing General Navy Hospital, China

  22. Experimental Results(1) ------ Lesion Detection

  23. Experimental Results(2) -------Retrieval • Comparative results demonstrate that LBP outperforms four 3D texture methods in terms of retrieval precision and processing speed.

  24. Experimental Results(3) -------Query time • The query time with VOI selection offers 4 times faster operation than that without.

  25. 3D Brain Visualization(1)

  26. 3D Brain Visualization(2)

  27. CBIR for 3D Brain Image------ On-line system(1):

  28. CBIR for 3D Brain Image------ On-line system(2): • Server side: • 100 3D brain images(DICOM format to JPG format) • 3D visual feature extraction(4 methods): Off-line pre-processing using Matlab. • 3D visualization: using Matlab • Client side: • Interface based on PHP generate dynamic web pages.

  29. PART IIIConclusion and Future Work

  30. Conclusion for MIRAGE (Middlesex medical Image Repository with a CBIR ArchivinGEnvironment) • Create Middlesex medical Image repository ( ~100000 2D images and 100 3D brain images) • Create CBIR archiving environment for 2D and 3D medical images.

  31. Future Work(1)------Continue working on 3D Brain Image • Test on the larger dataset and enrich our repository • Research on clinical purpose (EC FP7) • ------ Collaborate with Neuro-imaging Centre at Beijing General Navy Hospital, China.

  32. Future Work(2)------Echocardiogram Video Clip • Enrich our repository • Research for clinical purpose(EC FP7) • ------ Collaborate with First Hospital • of Tsinghua University, China. B-mode 2D Video Clip B-mode and M-mode Video clip UltrasonixTABLET Ultrasound scanner Colour Doppler Video Clip

  33. Future Work(3) ------- Grid Computing MDX Grid Machine

  34. PART VIDemonstration

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