Analysis of dental images using artificial immune systems
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. © 2006 IEEE.
2006 IEEE Congress on Evolutionary Computation, CEC 2006
Ji, Z., Dasgupta, D., Yang, Z., & Teng, H. (2006). Analysis of dental images using artificial immune systems. 2006 IEEE Congress on Evolutionary Computation, CEC 2006, 528-535. Retrieved from https://digitalcommons.memphis.edu/facpubs/2503