In this paper, we propose a local region-scalable active contour with expandable kernel for image segmentation. We call it LREK active contour. Our model uses intensity values of pixels on a set of scalable kernels along evolving contour. These kernels are to direct contour front towards object's boundary within an image domain. Key feature of our model is that scale of the kernels increases gradually until the boundary is detected. So, our LREK may reach the boundary faster than some other methods. We compare performance of our LREK to existing region-based models that using local region descriptor. Experimental results show more desirable segmentation outcomes of our method. Our LREK performs effectively in segmenting noisy, concave boundary, non-uniform, and heterogeneous textures objects with a large capture range and fast convergence. Moreover, our Gaussian LREK is able to trace blur or smooth boundary.
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