Transcript PPT - Middlesex University

TEXTURE-BASED 3D IMAGE RETRIEVAL
FOR MEDICAL APPLICATIONS
X. Gao, Y. Qian, M. Loomes, R. Comley, B. Barn , A. Chapman , J. Rix
Middlesex University, UK
R. Hui
Department of Neurosurgery, General Navy Hospital, P.R.China
MIRAGE
(Middlesex medical Image Repository with a CBIR ArchivinG Environment)
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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.
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http://image.mdx.ac.uk/
JSIC
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Innovation in the use of ICT for education and research.
http://www.jisc.ac.uk/
Proposed Framework for MRIAGE
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
Framework of Content-Based Image Retrieval
Texture-based 3D Brain Image Retrieval
Current Content-Based Image Retrieval
(CBIR)
Content-based image retrieval system
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QBIC, Nectar, Viper, etc.
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Visual feature extraction from 2D image
Content-based 3D Brain Image Retrieval
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Shape-based
Proposed framework for 3D image retrieval
3D Texture Feature Extraction
1) 3D Grey Level Co-occurrence Matrices (3D GLCM)
2) 3D Wavelet Transform (3D WT)
3) 3D Gabor Transform (3D GT)
4) 3D Local Binary Pattern (3D LBP)
1) 3D Grey Level Co-occurrence Matrices (3D
GLCM)
3D GLCM is two dimensional matrices of the joint
probability of occurrence of a pair of gray values
separated by a displacement d = (dx,dy,dz).
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52 Displacement vectors:
4 distance * 13 direction = 52
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4 Haralick texture features:
energy, entropy, contrast and homogeneity
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Feature vector:
208 components (=4 (features) * 52 (matrices)).
2) 3D Wavelet Transform (3D WT)
3D WT provides a spatial and frequency representation
of a volumetric image.
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2 scales of 3D WT
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Mean and Standard deviation
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Feature vector:
30 components (2 (features) +15 (sub-bands))
3) 3D Gabor Transform (3D GT)
3D GT generates a set of 3D Gabor filters
Gabor filters
g  x, y, z , F ,  ,    g  x, y, z  exp  j 2 F sin  cos x  F sin  sin y  F cos z 
^
Gabor Transform:
GTi  f x, y, z  * g x, y, z, Fi ,i , i  i  1,2,3...144
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144 Gabor filters
4 (F) *6(θ)*6(Φ) =144
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Mean and Standard deviation
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Feature vector:
288 components (2 (features) +144(filters))
4) 3D Local Binary Pattern (3D LBP)
Local binary pattern(LBP) is a set of binary code to
define texture in a local neighbourhood. A histogram is
then generated to calculate the occurrences of different
binary patterns.
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59 binary patterns
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Feature vector:
177 components (=59(patterns)*3(planes)
Similarity Measurement
Histogram Intersection(3D LBP)
DQ, I    minQi , I i 
i
 Normalized Euclidean distance (3D GLCM,3D WT,3D GT)
DQ, I  
 Qi  I i 
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i
i
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2
Experiment Results
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
Conclusion and Future work
Four 3D texture methods are exploited and evaluated in 3D
MR image retrieval.
Future work:
Test on the larger dataset
Find the best 3D texture representations
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Feature dimension reduction
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Combinations of some texture descriptors
Plug 3D image retrieval into GIFT framework.
Thank You.