Affiliations: Institute of Biomedical Engineering, Xi'an Jiaotong
University, Xi'an, 710049, P.R. China
Note: [] Corresponding author: Pan Lin, Institute of Biomedical
Engineering, Xi'an Jiaotong University, Xi'an, 710049, P.R. China. Tel.: +86
029 82673410; E-mail: [email protected]
Abstract: Level set methods are useful tools for medical image segmentation.
In this paper, a novel image segmentation technique was developed that combines
region statistical information with the level set method. The classical level
set methods depend mainly on local edge-based image features to guide the
convergence of the contour. This makes the method sensitive to noise and the
initial estimate. The method has two features. The first is that it uses obtain
region-based information by a mixture model. The second feature is that we
combine region statistical information with curvature-based regularization
penalizing the length of curve. The method is useful for a large variety of
segmentation problems. We present some preliminary experimental results using
synthetic images and, magnetic resonance (MR), ultrasound (US), computed
tomography (CT) images to demonstrate our methods. The experimental results
show that by incorporating region statistical information into the level set
framework, an accurate and robust segmentation can be achieved.
Keywords: Level set method, region statistical information, mixture model, and Image segmentation
