Supervised Multispectral Image Segmentation using Active Contours

Active contours have been widely used as image segmentation methods. The use of level set theory has provided more flexibility and convenience for the implementation of active contours. However, traditional active contour models have some limitations on the segmentation of complicated images whose sub-regions consist of multiple components. The segmentation of multispectral images is even a more difficult problem. We propose an advanced active contour model using the statistics of image intensity based on a multivariate mixture density model. The proposed active contour model shows a robust segmentation capability on the images that traditional segmentation methods cannot properly partition. Numerical experiments with synthetic and real images are presented.