Matching an Elastic Model of Chromosomal Shape to Features on a Self-Organising Map
نویسندگان
چکیده
We describe a technique for matching a single, learned elastic model of the shape of normal chromosomes to chromosomal images. Our model has a hierarchical organisation, with increasingly coarse shape descriptions at higher levels. A problem of finding the model description most likely to have generated an image is reduced to one of matching the locations of model points to the locations of image features encoded on a Kohonen Self-Organising Map (SOM). During matching, coarse shape information is communicated between levels via fixed, viewpoint-independent transformations between object-based frames. After matching, frame parameters provide a compact, multi-scale description of important shape information. We propose using this information to train a neural network to recognise structurally damaged chromosomes.
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