The algorithm of formation of a training set for an artificial neural network for image segmentation

This article suggests an algorithm of formation a training set for artificial neural network in case of image segmentation. The distinctive feature of this algorithm is that it using only one image for segmentation. The segmentation performs using three-layer perceptron. The main method of the segmentation is a method of region growing. Neural network is using for get a decision to include pixel into an area or not. Impulse noise is using for generation of a training set. Pixels damaged by noise are not related to the same region. Suggested method has been tested with help of computer experiment in automatic and interactive modes.

I. Introduction

Image segmentation is one of the variants of the task of clustering a set of pixels on the basis of color affinity. It should be pointed out that the segmentation problem does not have an exact solution and the effectiveness of the decision is usually evaluated on the basis of comparison with the results obtained by experts in manual mode. That means that the effectiveness of the segmentation cannot be unambiguous. One of the approaches actively developing in recent few years is the use of artificial neural networks. However, this approach faces the problem of the formation of a learning set. If there is a series of similar images, for example, with medical information, then the problem is solved quite simply. But if the task is to segment one single image, which has no analogues, then the traditional approaches face unsolvable difficulties. To solve the image segmentation problem, various artificial neural networks were used. There were built algorithms based on the multilayer perceptron [1], as well as its modifications using cross entropy [2], genetic algorithms [3], [4], regions growing [5], [6], minimal difference ratio [7], wavelet decomposition of the original image [8]. Additionally, multilayer perceptron was used in conjunction with other segmentation algorithms, for example, the k-means method [9]. A number of algorithms were also developed on the basis of Kohonen self-organizing maps [10]–[12]. As well as for a multilayer perceptron, there are some algorithms were proposed to combine Kohonen maps with other segmentation algorithms – the hybrid genetic algorithm [13], the k-means method [14], the growing of the regions [15]. All the algorithms listed above has specialized nature and oriented to images of a certain type. This specialization is necessary for the formation of a learning set. Neural networks trained on the one type of images do not work for other types of images. In this paper, we propose an algorithm for forming a learning set in a segmentation problem that is oriented to one particular image and does not require an additional set of similar images.