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Addressing the Medical Image Annotation Task using visual words representation. Uri Avni, Tel Aviv University, Israel Hayit GreenspanTel Aviv University, Israel Jacob GoldbergerBar Ilan University, Israel. Outline. Challenge description Proposed system Image representation - PowerPoint PPT Presentation
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Addressing the Medical Image Annotation Task using visual words representation
Uri Avni , Tel Aviv University, IsraelHayit Greenspan Tel Aviv University, Israel Jacob Goldberger Bar Ilan University, Israel
Outlineo Challenge description
o Proposed systemo Image representationo classification
o Results o Parameters optimizationo Performance analysis
o Conclusion
ImageClef 2009 medical annotation challenge12,677 classified x-ray images, 1733 unknown
images
Classification according to four labeling sets:o 57 classes o 116 classeso 116 IRMA codeso 196 IRMA codes
• Noisy images• Irregular brightness, contrast• Non-uniform class distribution
IRMA database
The IRMA group - Aachen University of Technology (RWTH), Germany
0 20 40 60 80 100 1200
500
1000
1500
2000Training data categories
Category number
Freq
uenc
y
• Great intra-class variability
Category #: 1121-230-961-700 Sagittal, Mediolateral, Left hip
IRMA Database - samples
Category #1121-110-500-000overview imageposteroanterior (PA)
Category #1123-112-500-000high beam energy posteroanterior (PA),expiration
Category #1123-121-500-000high beam energyanteroposterior (AP),inspiration
Category #1121-127-500-000overview imageanteroposterior (AP), supine
IRMA Database - samples• Great inter-class
similarity
Outlineo Challenge description
o Proposed systemo Image representationo classification
o Results o Parameters optimizationo Performance analysis
o Conclusion
Image representation o Move from 2D image to a vector of numbers
o Representation should preserve enough information of the image content
o Should be not sensitive to translation, artifacts and noise
o Compare and classify the compact representation
0 100 200
0
0.02
0.04
Word number
Image model
Patch extraction
• Extract raw pixels from patches of fixed size
• Dense sampling, ~200,000 patches per image • Normalize intensity, variance
• Ignore empty patches• Sample several images – one collection with
millions of patches
Feature space description- Reduce dimension of the collection
- Add position (x,y) to the features, position weight is important
- 8 dimensional feature vector
9x9 pixels PCA 6 coefficients
Dictionary
Build dictionary• Select k feature vectors as far apart as
possible
• Run k-means clustering
Cluster centers , with x,yCluster centers
Image representation• Scan image – translate patches to words
histogram
Image Dictionary
0 50 1000
0.02
0.04
Word number
Probability
Image representation• Use multiple scales
0 50 1000
150 200 250 300
Classification• Examine knn classifier, with different distance metrics
2
2( , ) exp( )2i j
i jK
x x
x x
• Examine several SVM kernels:
• Radial basis function
• Chi-square
• Histogram intersection
• One-vs-one multiclass SVM classifier, with n(n-1)/2 binary classifiers
))(exp(),(2
n
in
in
in
in
ji xxxxxxK
),min(),( n
jn
inji xxxxK
Outlineo Our objective
o Proposed systemo Image representationo Retrieval & classification
o Results o Parameters optimizationo Performance analysis
o Conclusion and future work
Selecting classifier typeEffect of histogram distance metric in k-nearest neighbors vs svm classifier
SVM
Symmetric Kullback – Leibler divergence
Jeffery divergence
i i
ii
i
ii P
QQQPPQPSKL loglog),(
i ii
ii
ii
ii PQ
QQPQPPQPJD 2log2log),(
Selecting feature spaceEffect of parameters on classification accuracy, using 20 cross-validation experiments
0 2 4 6 885
86
87
88
89
90
91
92
93
(x,y) scale
% C
orre
ct
Spatial features
with x,y
No x,y 6 8 10
89
90
91
92
93
PCA components
% C
orre
ct
Selecting type of features - invariance / discriminative power tradeoff
Selecting features
Feature type Average % correct Standard dev
Raw patches 88.43 0.32SIFT* 90.80 0.41Normalized Patches 91.29 0.56
* Scale and rotation invariance are not desired
Running time
SIFT
Raw
0 100 200 300 400 500 600 700 800
Build dictionaryExtract featuresTrain classifierClassify
Minutes
12,677 imagesRunning on Intel daul quad core Xeon 2.33Ghz
Build dictionary
Extract features
Train classifier
classification time per image
Total (train + classify)
Raw 6 min 96.8 min 6 min 0.54 sec 126 minSIFT 10 min 597 min 6 min 3.32 sec 724 min
Selecting dictionary
200 400 600 800 1000 1200 1400 1600
88.5
89
89.5
90
90.5
91
91.5
92
Number of words
% C
orre
ctDictionary size
Selecting dictionary
200 400 600 800 1000 1200 1400 1600
88.5
89
89.5
90
90.5
91
91.5
92
Number of words
% C
orre
ctDictionary size
Using multiple dictionaries for 3 scales increases classification accuracy by 0.5%
200 400 600 800 1000 1200 1400 1600
88.5
89
89.5
90
90.5
91
91.5
92
Number of words
% C
orre
ctDictionary size
1 scale3 scales
Classification results – effect of kernelEffect of kernel function on SVM classifier, for optimal kernel parameters
Kernel Type % Correct1 Scale 3 Scales
Radial Basis 91.45 91.59Histogram Intersection 91.29 91.89Chi Square 91.62 91.95
Classification results – confusion matrixConfusion matrix of random 2000 test images (2007 labels)91.95% correct
Detected category
True
cat
egor
y
20 40 60 80 100
20
40
60
80
100
0
1
2
3
4
5
Submission to ImageClef 2009 medical annotation task
o One run submitted
o Use the same classifier for the 4 label sets (2005,2006,2007,2008)
o Ignore IRMA code hierarchy
o Don’t use wildcardsRun & error score
2005 2006 2007 2008 SUM
TAUbiomed 356 263 64.3 169.5 852.8
Conclusion & future worko Using visual words with simple
features and dense sampling is efficient and accurate in general x-ray annotation
o We are applying the system to pathology classifications of chest x-rays, together with Sheba Medical Center
Healthy Enlarged heart Lung filtrate Left+right effusion
Thank you.
