Level set based segmentation using local fitted images and inhomogeneity entropy

Abstract Precise image segmentation plays an important role in advancing the understanding of objects of interest, and facilitating the estimation of their morphological changes. In this paper, we proposed a level set method for segmenting desirable objects by introducing an inhomogeneity entropy descriptor and entropy weighted energy functional based on three different local fitted images. The introduced entropy aims to alleviate challenges caused by weak contrast and severe intensity inhomogeneity, while the energy functional is used to assess intensity differences between images to be processed and their local fitted versions. Minimization of the energy can evolve the level set function towards the boundaries of objects depicted on images. Segmentation experiments on synthetic and natural images as well as medical images demonstrated the developed method achieved reasonable segmentation accuracy, as compared to some existing level set methods.

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