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 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