Combining super-resolution and level sets for brain image segmentation
Farzaneh Elahifasaee · Chalmers Publication Library (Chalmers University of Technology) · 2012
The term Super resolution, resolution enhancement, is a process to increase the res-olution of an image. This improvement quality is due to sub-pixel shift of low resolution images from each other between images. In fact, each low resolution image has new information of the image and the main aim of super resolution is to combining these low resolution images to enhance the image resolution. Following this method, allows users that without any demand for additional hard-ware, overcoming the limitations of the imaging system. Moreover, the main goal of segmentation is to distinguish an object from background. Segmentation can do that by dividing pixels of an image into prominent image regions. By this way, a specific region is corresponding to individual objects or natural parts of objects. Segmentation can be used in different fields such as image compression and image editing. Various methods have been proposed to enhance the segmentation results. In this thesis combining super resolution and level set segmentation were compared to low res-olution images segmentation. The results show that segmentation of super resolution image has better result com-pared to segmentation of low resolution images. Acknowledgements I would like to thank my advisor Prof. Irene Gu for her patience, guidance, and mentor-ship throughout my thesis. It means a lot to me to have someone looking out for me and providing this opportunity to work under her supervision. I give special thanks to her for introducing this field of research to me and for her beneficial comments and insight throughout the thesis that makes me deeper, more efficient and more productive. Last but not least, I am grateful to my family and friends for their emotional support in these sometimes difficult years.