A Three Dimensional Bi-directional Self-Organizing Neural Network (3-DBDSONN) Architecture for Three Dimensional Object Extraction from a Noisy Perspective

A novel three dimensional bi-directional self-organizing neural network (3-DBDSONN) architecture with fuzzy context sensitive thresholding suitable for three-dimensional (3D) image processing applications, is proposed in this article. The proposed architecture comprises three interconnected three-dimensional fuzzy layers of neurons. It is devoid of any back-propagation algorithms for weight adjustments. An application of extraction of three-dimensional binary objects from various degrees of noisy background is demonstrated on one synthetic and two real life three-dimensional binary voxelized images. The efficiency of the proposed network as regards to immunity to different types of noise indicates encouraging avenues.