Abstract:
This paper proposes a novel active contour model called weighted kernel mapping
(WKM) model along with an extended watershed transformation (EWT) method
for the level set image segmentation, which is a hybrid model based on the global
and local intensity information. The proposed EWT method simulates a general
spring on a hill with a fountain process and a rainfall process, which can be
considered as an image pre-processing step for improving the image intensity
homogeneity and providing the weighted information to the level set function.
The WKM model involves two new energy functionals which are used to segment
the image in the low dimensional observation space and the higher dimensional
feature space respectively. The energy functional in the low dimensional space
is used to demonstrate that the proposed WKM model is right in theory. The
energy functional in the higher dimensional space obtains the region parameters
through the weighted kernel function by utilising mean shift technique. Since
the region parameters can better represent the values of the evolving regions
due to the better image homogeneity, the proposed method can more accurately
segment various types of images. Meanwhile, by adding the weighted information,
the level set elements can be updated faster and the image segmentation can
be achieved with fewer iterations. Experimental results on synthetic, medical
and natural images show that the proposed method can increase the accuracy
of image segmentation and reduce the iterations of level set evolution for image
segmentation.
