An application of genetic algorithms to geometric model-guided interpretation of brain anatomy

This work applies 3D Fourier Descriptors (FDs) and Genetic Algorithms (GAs) to the optimisation of the shape and position of models of anatomical objects within the human brain. Using magnetic resonance image data, we perform an approximate segmentation on one lateral ventricle and use the FDs from this as seeding values for the GAs to search for the left and right lateral ventricles in subsequent 3I) image data sets, showing that the method is capable of coping with normal biological variation within and between individuals. Finally, we compare the GA-guided segmentation with a manual region growing method and find an agreement of 79.9±5.8% in voxel classification with a corresponding mean edge placement error of 2.1±0.4 mm.