A level set model without initial contour

Lei He
William G. Wee
Songfeng Zheng, Missouri State University
Li Wang


This paper presents a new local edge-based level set model that does not use initial contours. Unlike traditional edge-based active contours that use gradient to detect edges, our model derives the neighborhood distribution and edge information with two different localized region-based operators: a Gaussian mixture model-based intensity distribution estimator and the Hueckel operator. We incorporate the operator outcomes into the recently proposed local binary fitting (LBF) model as local distribution fitting (LDF) model, which enables a model without the initial contour selection, i.e., the level set function can be initialized with a random constant instead of a distance map. Thus our model overcomes the initialization sensitivity problem of most active contours. In addition, with region-based edge detection, the proposed LDF model provides more accurate and robust segmentation. Experiments on both synthetic and real images show the improved performance of our proposed model over the LBF model.