Automatic image segmentation by graph cuts for bio-medical applications

Graph cut image partitioning is used to segment any type of the image data. The image data is transformed by a kernel function so that the piecewise constant model of the graph cut formulation becomes applicable. The method affords an effective alternative to complex modeling of the original image data while taking advantage of the computational benefits of graph cuts. Using a common kernel function, energy minimization typically consists of iterating image partitioning by graph cut iterations and evaluations of region parameters via fixed point computation. The method results in good segmentations and runs faster the graph cut methods. The segmentation from MRI data is an important but time consuming task performed manually by medical ex- perts. In this method, a semi-automatic interactive segmentation system with the ability to adjust operator control is achieved. The energy is efficiently minimized using graph cut.