Paper
24 June 1998 Physics-based model of the Kohonen ring
Petia Radeva, Jordi Guerrero, M. Carmen Molina, Roger Serneels
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Abstract
In this paper, we introduce a new segmentation technique (called Kohonen snake) based on the neural simulation of deformable models designed to reconstruct 3D objects. Kohonen snake possesses all properties of Kohonen networks (lateral interaction during the learning process, topologically preserving mapping) and of deformable models (namely, elastic properties). Elastic properties of the physics-based Kohonen ring improves the shortcomings of the Kohonen network related to twisting, `dead' neurons, accumulation and rounding the network, whereas the data- driven approach of Kohonen snake improves the problem of initialization and local minima of the snakes. When integrating both models, the first question is how to combine their parameters. We simulate the Kohonen snake behavior with different parameter values using sequential and parallel weight updating, study the need of decreasing the parameters and of reordering image features. As a result, we conclude that Kohonen snake has better control on its shape that makes it less dependent on the values of its parameters and initial conditions. Our tests on segmentation of synthetic and real images illustrate the usefulness of the Kohonen snake technique.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Petia Radeva, Jordi Guerrero, M. Carmen Molina, and Roger Serneels "Physics-based model of the Kohonen ring", Proc. SPIE 3338, Medical Imaging 1998: Image Processing, (24 June 1998); https://doi.org/10.1117/12.310865
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Cited by 1 scholarly publication.
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KEYWORDS
Neurons

Image segmentation

Brain mapping

3D modeling

Data modeling

Neural networks

Instrument modeling

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