Analysis of Dental Images using Artificial Immune Systems
Zhou Ji
Dasgupta, D.
Zhiling Yang
Hongmei Teng
Univ. of Memphis, Memphis;
This paper appears in: Evolutionary Computation, 2006. CEC 2006. IEEE Congress on
Publication Date: 0-0 0
On page(s): 528-535
Location: Vancouver, BC,
ISBN: 0-7803-9487-9
INSPEC Accession Number: 9723494
Digital Object Identifier: 10.1109/CEC.2006.1688355
Current Version Published: 2006-09-11
Abstract
This paper introduces a preliminary effort to develop an automatic image analysis method using artificial immune systems for clinical dental diagnosis. To diagnose dental deformity, especially malocclusion, manual measurement of certain geometry on the X-ray images is traditionally used, which relies on subjective judgment to determine the reference points. This paper proposes a feature extraction method that is based on the brightness distribution of the image instead of the anatomical parts. A negative selection algorithm is then applied to the data represented as real-valued vectors to detect the cases of severe malocclusion. Using the same data representation, one-class SVM was also tried to compare the detection capability with the negative selection algorithm. The results show that the negative selection algorithm appears more suitable for this problem.
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