Abstract
The segmentation of intensity inhomogeneity images is always a challenging problem. There are two kinds of intensity inhomogeneities, one associated with the imaging devices and illumination variations, and the other associated with the essential characteristics of the intensities in objects and backgrounds. We name the second kind of intensity inhomogeneity as intensity complexity. In this paper, we focus on the segmentation of the images with intensity complexity. Our main argument is to quantify the complex intensities and convert them into useful features to improve segmentation accuracy. Two new quantities called the region intensity complexity index (RIC-Index) and factor (RIC-Factor) are introduced to quantify the intensity complexity. Then the quantified intensity complexity is incorporated into a variational level set framework. The total energy functional of the proposed framework consists of the following three items: a region intensity complexity term, a local region fitting energy term, and an edge-based energy term. The first term is defined by exploiting the region intensity complexity factor of the images. Mean and variance are utilized in the local region fitting energy to describe the image texture information. The last term of the energy functional, which is also derived from the region intensity complexity factor, incorporates the significant edge information. By integrating these three terms, the proposed model can handle intensity complexity images, especially two kinds of images: one with complex intensities in the objects, and the other with complex intensities in the backgrounds. The experimental results on 40 intensity complexity images and 1000 natural images from the Extended Complex Scene Saliency Dataset have indicated that our proposed algorithm can produce satisfactory segmentation results in comparison with five state-of-the-art methods and a deep learning approach.
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Acknowledgements
This work is supported by National Nature Science Foundation of China (Nos. 11531005, 11971229) and Science Foundation of Zhejiang Sci-Tech University (ZSTU) (No. 19062406-Y).
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Li, X., Liu, H. & Yang, X. Region intensity complexity active contours. Multidim Syst Sign Process 31, 1185–1206 (2020). https://doi.org/10.1007/s11045-020-00704-5
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DOI: https://doi.org/10.1007/s11045-020-00704-5