A multichannel algorithm for image segmentation with iterative feedback

In this paper we present a segmentation algorithm for multichannel image analysis. It is based on a novel method that significantly improves the segmentation performance with respect to both homogeneity of the segmented regions and precision of the segmented region boundaries. The algorithm yields excellent results in comparsion with other segmentation algorthms that are based on feature space clustering followed by minimum distance classification, as will be shown in some segmentation examples. The main idea of the algorthm proposed here is the iterative feedback of the knowledge about the analysed image that has been obtained from preceding segmentation results. It needs just a stack of feature images and the indication of the number of required classes for input data. Therefore, it has a broad field of possible applications, especially in multichannel image analysis.