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Middlesex Medical Image Repository Dr. Yu Qian [email protected]
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 PART I
Introduction of MIRAGE Project MIRAGE (Middlesex medical Image
Repository with a CBIR ArchivinG Environment)
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. JISC Innovation in the use of ICT for education
and research. PART II Content-Based Image Retrieval (CBIR)
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: Colour-Based Retrieval Texture-Based Retrieval Shape-Based
Retrieval Query by Feature Query by Sketch Framework of
Content-Based Image Retrieval Compared with Text-Based Image
Retrieval(TBIR)
Advantage Disadvantage TBIR Semanticinformation Heavy labour and
time consumption Visual information scarcity Subjectivity Language
problem CBIR Less time and labour intensity Objective retrieval
results Semanticgap 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) PART III Framework for MIRAGE Proposed Framework for MRIAGE
1) CBIR for 2D Medical Image ----GIFT 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: GIFT Framework 2) CBIR for
3D Medical Images Proposed Framework for 3D Image Retrieval 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)
Similarity Measurement
Histogram Intersection(3D LBP) Normalized Euclidean distance (3D
GLCM,3D WT,3D GT) ExperimentResults Processing and Query time
Methods Processing time Query time 3D GLCM 10.65s 0.83s 3D WT 2.03s
0.11s 3D GT 14.3m 0.31s 3D LBP 0.78s 0.29s 3) Image Labelling Image
labelling PART IV What We Have Done and Future Work 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+ Future Work Continue working on image
labelling
Plug 3D image retrieval into GIFT framework System evaluation Final
report Question?Thanks