Atlas and snake based segmentation of organs at risk in radiotherapy in head MRIs

Automatic segmentation of organs at risk in head Magnetic Resonance Images (MRI) is a challenging task it necessitates accurate definition of organs at risk (OAR). This crucial step is time consuming and prone to inter and intra-observer variations. Automatic segmentation by atlas deformable registration may help to reduce time and variations. the atlas based segmentation of brain OAR may suffer from normal individual variations in human brain structures in the other hand deformable models (Snakes) are much more accurate to locate object or structures boundaries but they are very sensitive to initialization and the computational cost of calculating the force field that will deform the snake to these boundaries. in this paper we present a method that combine the robustness of atlas based segmentation methods and the accuracy of Snakes and we use filters to improve the quality of final segmentation. we use an unbiased age appropriate MRI atlas template the ICBM 152 and Multigrid GVF to avoid high computational costs of GVF Snakes filters like Canny filter aim to reduce irrelevant data from MRI images and improve convergence and the final structure segmentation.