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Analysis of Dental Images using Artificial Immune Systems
Zhou Ji1, Dipankar Dasgupta1, Zhiling Yang2 & Hongmei Teng1
1: The University of Memphis2: Yinchuan Stomatological Hospital, China
CEC 2006. Vancouver, BC, Canada. July 17, 2006.
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outline
Application background AIS method Data preprocessing Preliminary results
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Application background
Occlusion: alignment of teeth/jaw Malocclusion
Abnormal occlusion Diagnosis using X-ray
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malocclusion
Different types: I (normal bite), II (overbite), and III (underbite)
Mild or severe (functional)
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lateral view skull X-ray
Normal case Example of malocclusion
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conventional diagnosis methodAngle’s classification: angle ANB (3 in the picture)
N
A
B
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AIS method
Negative selection algorithmsA detector set is generated from normal
samples and used to detect abnormal cases. One-class classification: classification
between two classes using samples from one class to train the systemanomaly detection
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V-detector
A new negative selection algorithmMaximized detection size of detectorsCoverage estimate
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Data preprocessing method-feature extraction
Using brightness distribution instead of traditional feature extraction (identification of entities or anatomical parts)
Binarization at multiple thresholds Description of each binary image with four
real numbers
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remove artificial parts
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binarization using multiple thresholds
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choose thresholds
T0 = Vmax,
T1 = Vmax − (Vmax − Vmin)/n , ..., Tn-1 = Vmax − (n − 1)(Vmax − Vmin)/n ,
Thresholds are decided by the actual values of the image.
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decide reference point
Binarized at the highest threshold
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extract four featuresat each threshold (or for each binary image)
1. Horizontal displacementx = xwhite − x0,
(xwhite is the mean of x of white pixels)
1. Vertical displacementy = ywhite − y0,
(ywhite is the mean of y of white pixels)
1. Displacement distancer = mean of distances between white pixels to (x0, y0)
1. Area massA = total number of white pixel/width · height
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Steps to represent an image as a real-valued vector
A grayscale image n binary images Each binary image 4 real values
1. Clean up 2. Binarization3. Calculate 4 features4. Normalization
Result: a grayscale image is represented by a 4n-dimensional vector over [0,1]4n
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Preliminary experiment results
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compare with SVM
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Using half of normal data to train
SVM result
V-detector result
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Summary
V-detector shows some potentials. A novel feature extraction is proposed.
Key idea: general shapes instead of anatomical part Issues:
Other possible feature representations more normal data are desired.
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Thank you!
Questions?