Automatic image segmentation based on level set approach: application to brain tumor segmentation in MR images
Xiaobing Li · OpenGrey (Institut de l'Information Scientifique et Technique) · 2009
The aim of this dissertation is to develop an automatic segmentation of brain tumors from MRI volume based on the technique of "level sets". The term "automatic" uses the fact that the normal brain is symmetrical and the localization of asymmetrical regions permits to estimate the initial contour of the tumor. The first step is preprocessing, which is to correct the intensity inhomogeneity of volume MRI and spatially realign the MRI volumes of the same patient at different moments. The plan hemispherical brain is then calculated by maximizing the degree of similarity between the half of the volume and his reflexion. The initial contour of the tumor can be extracted from the asymmetry between the two hemispheres. This initial contour is evolved and refined by the technique "level set" in order to find the real contour of the tumor. The criteria for stopping the evolution have been proposed and based on the properties of the tumor. Finally, the contour of the tumor is projected onto the adjacent images to form the new initial contours. This process is iterated on all slices to obtain the segmentation of the tumor in 3D. The proposed system is used to follow up patients throughout the medical treatment period, with examinations every four months, allowing the physician to monitor the state of development of the tumor and evaluate the effectiveness of the therapy. The method was quantitatively evaluated by comparison with manual tracings experts. Good results are obtained on real MRI images