  Active contour models have achieved prominent success in the area of image
segmentation, allowing complex objects to be segmented from the background for
further analysis. Existing models can be divided into region-based active
contour models and edge-based active contour models. However, both models use
direct image data to achieve segmentation and face many challenging problems in
terms of the initial contour position, noise sensitivity, local minima and
inefficiency owing to the in-homogeneity of image intensities. The saliency map
of an image changes the image representation, making it more visual and
meaningful. In this study, we propose a novel model that uses the advantages of
a saliency map with local image information (LIF) and overcomes the drawbacks
of previous models. The proposed model is driven by a saliency map of an image
and the local image information to enhance the progress of the active contour
models. In this model, the saliency map of an image is first computed to find
the saliency driven local fitting energy. Then, the saliency-driven local
fitting energy is combined with the LIF model, resulting in a final novel
energy functional. This final energy functional is formulated through a level
set formulation, and regulation terms are added to evolve the contour more
precisely across the object boundaries. The quality of the proposed method was
verified on different synthetic images, real images and publicly available
datasets, including medical images. The image segmentation results, and
quantitative comparisons confirmed the contour initialization independence,
noise insensitivity, and superior segmentation accuracy of the proposed model
in comparison to the other segmentation models.
