Piecewise Constant Level Set Method for 3D Image Segmentation

Level set methods have been proven to be efficient tools for tracing interface problems. Recently, some variants of the Osher- Sethian level set methods, which are called the Piecewise Constant Level Set Methods (PCLSM), have been proposed for some interface problems. The methods need to minimize a smooth cost functional under some special constraints. In this paper a PCLSM for 3D image segmentation is tested. The algorithm uses the gradient descent method combined with a Quasi-Newton method to minimize an augmented Lagrangian functional. Experiments for medical image segmentation are shown on synthetic three dimensional MR brain images. The efficiency of the algorithm and the quality of the obtained images are demonstrated. 1 Piecewise constant level set methods In many applications one wants to divide an image into subsections based on intensity values, e.g. extract the gray matter, white matter and cerebrospinal fluid from a brain MRI, recognize the letters on a car plate or isolat...

• Type: Conference
• Year: 2007
• Where: SCALESPACE
• Authors: Are Losnegård, Oddvar Christiansen, Xue-Cheng Tai
• Keywords: Level Set, Level Set Methods, Piecewise Constant Level, SCALESPACE 2007