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 comparison with other segmentation algorithms that are based on feature space clustering followed by minimum distance classification, as is shown in some segmentation examples. The main idea of the algorithm 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.
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feedback,image classification,image segmentation,iterative methods,telecommunication channels,feature images,feature space clustering,image segmentation algorithm,input data,iterative feedback,minimum distance classification,multichannel algorithm,multichannel image analysis,segmentation performance,segmented region boundaries
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