Abstract
The algorithm of active contour model is an image segmentation method based on curve evolution theory, which have great flexibility, adaptability and separation accuracy. Accurate segmentation of inhomogeneous image targets has always been a difficult issue in image segmentation field. In this paper, an improved Chan-Vese model based on local information is proposed, which utilizes both global and local image information. Combining the local binary fitting (LBF) model with the retinex model, this paper redefines the fit of the Chan-Vese model. And adding a weight coefficient, so that the fitting term adaptively calculates the respective weights of the global and local information. The experimental results on various image data show that the proposed method can achieve more accurate segmentation results.
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