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Content-based Retrieval of 3D Medical Images
Y. Qian, X. Gao , M. Loomes, R. Comley, B. Barn
School of Engineering and Information Sciences
Middlesex University, UK
R. Hui, Z.Tian
Department of Neurosurgery, General Navy Hospital, P.R.China
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.
So far 100,000 2D images and 100 images in 3D form.
http://image.mdx.ac.uk/
JSIC
Innovation in the use of ICT for education and research.
http://www.jisc.ac.uk/
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.
e.g. Query by Example Image(QBE)
GIFT Framework
GIFT(GNU Image Finding Tool) 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)
Demo:
Content-Based 3D Brain Image Retrieval
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
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
Extraction of Volumetric Textures
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).
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)).
2) 3D Wavelet Transform (3D WT)
3D WT provides a spatial and frequency representation of a volumetric image.
2 scales of 3D WT
Mean and Standard deviation
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
Gabor Transform:
144 Gabor filters
4 (F) *6(θ)*6(Φ) =144
Mean and Standard deviation
Feature vector:
288 components (2 (features) +144(filters))
zFyFxFjzyxgFzyxg cossinsincossin2exp,,,,,,,^
iiii FzyxgzyxfGT ,,,,,*,, 144...3,2,1i
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.
59 binary patterns
Feature vector:
177 components (=59(patterns)*3(planes)
Similarity Measurement
Histogram Intersection(3D LBP)
Normalized Euclidean distance (3D GLCM,3D WT,3D GT)
i
ii IQIQD ,min,
2
,
i i
ii IQIQD
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
1) Conclusion:
Comparative results demonstrate that LBP outperforms four 3D texture methods in terms of retrieval precision and processing speed.
The query time with VOI selection offers 4 times faster operation than that without.
2) Future work:
Test on the larger dataset
Plug 3D image retrieval into GIFT framework (MIRAGE 2011)