<title>Neural network architecture for automatic chromosome analysis</title>

Abstract

We are interested in designing a neural network system for automatic chromosome. The goal of this approach is to make the chromosome regions more salient and more interpretable to human skilled technicians than they are in the original imagery. The proposed segmentation model is based upon the biologically derived boundary contour system (BCS) of Grossberg and Mingolla. The practical application of the model to real images raises an important problem. The boundaries generated by BCS have a sizable thickness that is a function of the contrast gradient between two adjacent regions. In order to solve this problem we propose the use of a feedback diffusion. The image resultant of the diffusion is fed back to the simple cell layer. Furthermore, the boundary representation is also fed back to the boundary segmentation stage. In this way, the boundaries are adapted to the variations produced by the feedback diffusion, achieving a gradual boundary thinning. We also propose a modificated diffusive filling-in equation for obtaining better results in homogeneous regions. The behavior of the Grossberg-Todorovic's equation reduces the homogenizing of the regions contained inside the boundaries. In order to solve this problem we introduce a new parameter, rho, called recovery parameter. This parameter regulates the activity variation margin of a node with respect to its initial value. With regard to the improvement in homogenizing, with a value for parameter rho near to zero, the resulting regions present a plain surface, making easy the chromosome bands separation.