Nonparametric Image Segmentation Using Rényi’s Statistical Dependence Measure

Abstract

In this paper, we present a novel nonparametric active region model for image segmentation. This model partitions an image by maximizing the similarity between the image and a label image, which is generated by setting different constants as the intensities of partitioned subregions. The intensities of these two images can not be compared directly as they are of different modalities. In this work we use Renyi’s statistical dependence measure, maximum cross correlation, as a criterion to measure their similarity. By using this measure, the proposed model deals directly with independent samples and does not need to estimate the continuous joint probability density function. Moreover, the computation is further simplified by using the theory of reproducing kernel Hilbert spaces. Experimental results based on medical and synthetic images are provided to demonstrate the effectiveness of the proposed method.