Astronomical image segmentation by self-organizing neural networks and wavelets.

Standard image segmentation methods may not be able to segment astronomical images because their special nature. We present an algorithm for astronomical image segmentation based on self-organizing neural networks and wavelets. We begin by performing wavelet decomposition of the image. The segmentation process has two steps. In the first we separate the stars and other prominent objects using the second plane (w(2)) of the wavelet decomposition, which has little noise but retains enough signal to represent those objects. This method was as least as effective as the traditional source extraction methods in isolating bright objects both from the background and from extended sources. In the second step the rest of the image (extended sources and background) is segmented using a self-organizing neural network. The result is a predetermined number of clusters, which we associate with extended regions plus a small region for each star or bright object. We have applied the algorithm to segment images of both galaxies and planets. The results show that the simultaneous use of all the scales in the self-organizing neural network helps the segmentation process, since it takes into account not only the intensity level, but also both the high and low frequencies present in the image. The connectivity of the regions obtained also shows that the algorithm is robust in the presence of noise. The method can also be applied to restored images.