Abstract
Magnetic Resonance Imaging provides a non-invasive means to study the neural correlates of Fetal Alcohol Spectrum Disorder (FASD) - the most common form of preventable mental retardation worldwide. One approach aims to detect brain abnormalities through an assessment of volume and shape of two sub-cortical structures, the caudate nucleus and hippocampus. We present a method for automatically segmenting these structures from high-resolution MR images captured as part of an ongoing study into the neural correlates of FASD.
 
 Our method incorporates an Active Shape Model, which is used to learn shape variation from manually segmented training data. A modified discrete Geometrically Deformable Model is used to generate point correspondence between training models. An ASM is then created from the landmark points. Experiments were conducted on the image search phase of ASM segmentation, in order to find the technique best suited to segmentation of the hippocampus and caudate nucleus. Various popular image search techniques were tested, including an edge detection method and a method based on grey profile Mahalanobis distance measurement. A novel heuristic image search method was also developed and tested. This heuristic method improves image segmentation by taking advantage of characteristics specific to the target data, such as a relatively homogeneous tissue colour in target structures.
 
 Results show that ASMs that use the heuristic image search technique produce the most accurate segmentations. An ASM constructed using this technique will enable researchers to quickly, reliably, and automatically segment test data for use in the FASD study.
Highlights
Fetal Alcohol Spectrum Disorder (FASD) is especially prevalent amongst residents of the Western Cape region of South Africa
We attempt to find the best algorithm for the automatic segmentation of the caudate nucleus and hippocampus from our Magnetic Resonance Imaging (MRI) test data, within the context of a study into the neural correlates of FASD
We implement an Active Shape Model (ASM) that incorporates various popular image search methods to find target structure boundaries. In addition to these methods, we develop our own heuristic image search method - tailored to work well with the abovementioned target structures
Summary
Fetal Alcohol Spectrum Disorder (FASD) is especially prevalent amongst residents of the Western Cape region of South Africa. This disorder affects the embryos of women who ingest alcohol whilst pregnant. The problem with standard GDMs is that they can deform to arbitrary shapes that are not representative of the class of shapes that they are designed to fit This problem is especially prevalent when segmenting noisy data, which contain many false positives. Shape priors, such as surface smoothness constraints, are sometimes used to limit deformation to a shape that is geometrically similar to the original model, but it is still possible for models to deform into suboptimal shapes [3]
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