Image Segmentation by Graph Cuts via Energy Minimization

Multiregion graph cut image partitioning via kernel mapping 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 objective function contains an original data term to evaluate the deviation of the transformed data within each segmentation region, from the piecewise constant model, and a smoothness boundary preserving regularization term. 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. This process is challenging due to the high diversity in appearance of tissue among the patient. In this method, a semi-automatic interactive brain segmentation system with the ability to adjust operator control is achieved. The energy is efficiently minimized using graph cut.