Abstract

In this paper, by employing the cosine function to express the so-called data fitting term in traditional active contour models, we propose an active contour model with the global cosine fitting energy for segmenting synthetic and real-world images. After that, in order to segment the image with intensity inhomogeneity, we extend the proposed global model to the local cosine fitting energy. In addition, we introduce level set regularization terms into the proposed models to avoid the expensive computational cost which is usually caused by the reinitialization of the evolving level set function. Experimental results indicate that the proposed models are accurate and effective when applied to segment different types of images. Moreover, our models are more efficient and robust for segmenting the images with strong noise and clutter than the Chan–Vese model and the local binary fitting model.

© 2015 Optical Society of America

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