Shape extraction of volumetric images of filamentous bacteria using topology adaptive self organization

The study of the filamentous objects in waste water has gained momentum due to its significant effect in environmental pollution. The paper describes a neural network based skeleton extraction technique for volumetric images of these biofilm objects. These objects require huge computer storage space. One way to economize the storage space is to represent such images in the form of a vector skeleton (a piecewise linear approximation). Such a skeleton preserves the essential structure of the object. The proposed neural network does not start with a predefined net topology. The topology evolves during the learning process on the basis of the input. The present technique has certain advantages over the conventional 3-D thinning techniques. It achieves data reduction at a higher rate. Also, the proposed technique is highly robust to noise and arbitrary rotations of an image