Segmentation of TEM images using oscillatory neural networks

Oscillatory neural networks are a relatively recent approach for the problem of image segmentation. Inside of this context, the oscillator neuron of Terman-Wang is presented, which is used as base element of an oscillatory network called LEGION (Locally Excitatory Globally Inhibitory Oscillator Network). The continuous version of the LEGION network, based on a set of differential equations, presents high computational complexity and has limited capacity of segmentation, which restricts its practical application, being adequate for implementation in parallel hardware topologies. To reduce the computational complexity in serial computers, an algorithm proposed by Terman and Wang is presented, which implies significant gain of speed in comparison to the continuous version and, in contrast, capacity to discriminate a unlimited number of segments. An interactive version of this algorithm was proposed and the results obtained in segmentation of transmission electron microscopy (TEM) images were evaluated, with the objective of measuring helium bubbles in silicon samples. As final result we found that the LEGION network presents itself as a singular alternative to solve problems of image segmentation, which provides simultaneously both spatial and temporal discrimination of segments.