Research Article
Multiregion Image Segmentation by Graph Cuts for Brain Tumour Segmentation
@INPROCEEDINGS{10.1007/978-3-642-35615-5_51, author={R. Ramya and K. Jayanthi}, title={Multiregion Image Segmentation by Graph Cuts for Brain Tumour Segmentation}, proceedings={Third International conference on advances in communication, network and computing}, proceedings_a={CNC}, year={2012}, month={12}, keywords={Kernel function Graph cuts Image segmentation Brain tumour}, doi={10.1007/978-3-642-35615-5_51} }- R. Ramya
K. Jayanthi
Year: 2012
Multiregion Image Segmentation by Graph Cuts for Brain Tumour Segmentation
CNC
Springer
DOI: 10.1007/978-3-642-35615-5_51
Abstract
Multiregion graph cut image partitioning via kernel mapping is used to segment any type of the image data.The piecewise constant model of the graph cut formulation becomes applicable when the image data is transformed by a kernel function. 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. 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. The segmentation of MRI image is challenging due to the high diversity in appearance of tissue among thepatient.A semi-automatic interactive brain segmentation system with the ability to adjust operator control is achieved in this method.