analysis of dental images using artificial immune systems zhou ji 1, dipankar dasgupta 1, zhiling...
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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.
outline
Application background AIS method Data preprocessing Preliminary results
Application background
Occlusion: alignment of teeth/jaw Malocclusion
Abnormal occlusion Diagnosis using X-ray
malocclusion
Different types: I (normal bite), II (overbite), and III (underbite)
Mild or severe (functional)
lateral view skull X-ray
Normal case Example of malocclusion
conventional diagnosis methodAngle’s classification: angle ANB (3 in the picture)
N
A
B
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
V-detector
A new negative selection algorithmMaximized detection size of detectorsCoverage estimate
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
remove artificial parts
binarization using multiple thresholds
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.
decide reference point
Binarized at the highest threshold
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
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
Preliminary experiment results
compare with SVM
Using half of normal data to train
SVM result
V-detector result
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.
Thank you!
Questions?
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