| Abstract: | Segmentation of images with intensity inhomogeneity is a significant task
in the field of image processing, especially in medical image processing and analysis.
Some local region-based models work well on handling intensity inhomogeneity, but
they are always sensitive to contour initialization and high noise. In this paper, we
present an adaptive segmentation model for images with intensity inhomogeneity in
the form of partial differential equation. Firstly, a global intensity fitting term and a
local intensity fitting term are constructed by employing the global and local image
information, respectively. Secondly, a tradeoff function is defined to adjust adaptively
the weight between two fitting terms, which is based on the neighborhood contrast of
image pixel. Finally, a weighted regularization term related to local entropy is used to
ensure the smoothness of evolution curve. Meanwhile, a distance regularization term
is added for stable level set evolution. Experimental results show that the proposed
model without initial contour can segment inhomogeneous images stably and effectively, which thereby avoiding the influence of contour initialization on segmentation
results. Besides, the proposed model works better on noise images comparing with
two relevant segmentation models. |
