Transcript Poster III
An Efficient Multi-Resolution GA approach to Dental Image Alignment Diaa Eldin Nassar, Mythili Ogirala, Donald Adjeroh, and Hany Ammar Dental radiographs show 2D projections of teeth. Changes in the relative positions of the x-ray tube, the film and the teeth result in non-identical projections of the teeth. Aspect of the Image Alignment problem To compensate for the possible geometric discrepancies between a pair of subject/reference teeth images, the dental image alignment stage transforms the subject tooth image so that it becomes aligned to the reference tooth image. The decision-making stage then determines whether two aligned teeth images are matched or not. The MR-GA method Location and Orientation Features information of edge points The Automated Dental Identification System (ADIS) provides an automated web-based environment for matching unidentified deceased individuals to missing or wanted persons. Matching is based on comparison of dental records of missing or wanted persons against those obtained from the remains of deceased individuals. For each submitted subject record, ADIS generates a short “match-list” of candidates whose dental features are significantly close to those of the subject. The “short” match-list is then inspected by a forensic expert who makes a final decision on the identity of each unidentified individual. This Research is supported by the Criminal Justice Information Services division (CJIS) of the US Federal Bureau of Investigations (FBI), the US National Science Foundation (NSF) award number EIA-0131079, and the US National Institute of Justice (NIJ) award number 2001-RC-CX-K013. X a Transformation Affine: Y c Model 1 0 Similarity measure Search strategy b d 0 e x f y 1 1 Oriented Hausdorff distance: Multi-resolution Genetic Algorithm To estimate the values of the parameters a, b, c, d, e, f : • We start with the coarsest level and generate two random subpopulations of chromosomes that correspond to points in the affineparameter search space. • Each chromosome has 6 genes, each gene is 7 bits, and each subpopulation contains 8 chromosomes. • Reproduction rate is 10%, crossover rate is 70%, and migration each other generation at a rate of 30%. • We decode each chromosome to its corresponding parameters value using inverse linear mapping. • We evaluate the goodness of a chromosome, using the edge-oriented Hausdorff distance. • Genetic search at any scale continues as long as there is progress and the maximum number of generations at that scale is not exceeded. • Otherwise, we advance our GA search to the next scale after adjusting the horizontal and vertical translation parameters accordingly. • In effect, the alignment parameters are progressively refined as we move from coarser to finer scales, with fast convergence as a result of excluding the poorest chromosomes early at the coarsest scale. Empirical results show that the MR-GA approach is capable of timely correcting misalignments of up to 18.