Constrained Self-organizing Map for the Reconstruction of the Brain Lateral Ventricle
نویسندگان
چکیده
This paper presents a constrained self-organizing map (SOM) model for the visualization and reconstruction of the human brain lateral ventricle. The SOM model is a widely used method to approximate large and complex high dimensional data and reduce the data dimension for advanced applications. In our applications, the SOM model is used to deform a spherical network field to a 3D crooked brain lateral ventricular surface. The main disadvantages of the formal SOM algorithm are its difficulties in stretching the network inside the concave parts of the lateral ventricular surface. Hence, a constrained SOM model is proposed to first obtain an elementary model of lateral ventricle which can be easily mapped to the lateral ventricle later by the SOM model. Based on this method, the SOM network field can successively and precisely map to the brain lateral ventricular surface. The simulations on T1-weighted MR images show that the proposed algorithm is robust to reconstruct 3D meshed brain lateral ventricular structures and its application to 3D morphometry is practicable.
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