ADAPTIVE SEGMENTATION OF MEDICAL MR IMAGES BASED ON BIAS CORRECTION

A two-phase model is introduced to extract clinically useful information from medical MR images. In the preprocessing phase, a refined bias correction method is adopted to reduce the influence of intensity inhomogeneity by removing the bias field, which paves the way for improving the subsequent segmentation accuracy. During image segmentation process, a novel adaptive level set technique is designed to capture the boundary of desired region. By virtue of adaptive driving term, the external force automatically changes its propagating direction when evolving curve goes through object boundary, which effectively prevents the final results deviating from correct position. Moreover, insensitivity to initial contour also enables its automatic applications. Experiments on synthetic and real MR images demonstrate the feasibility and robustness of the proposed method.