A novel automatically initialized level set approach based on region correlation for lumbar vertebrae CT image segmentation

Despite recent advances, robust automatic segmentation for vertebrae computed tomography (CT) image still presents considerable challenges, mainly due to its inherent limitations, such as topological variation, irregular boundaries (double boundary, weak boundary) and image noises, etc. Therefore, this paper proposes a novel automatically initialized level set approach based on region correlation, which is able to deal with these problems in the segmentation. First, an automatically initialized level set function (AILSF) is designed to automatically generate a smooth initial contour. This AILSF comprises hybrid morphological filter (HMF) and Gaussian mixture model (GMM), which can guarantee the initial contour precisely adjacent to the object boundary. Second, we introduce a region correlation based level set formulation, which simultaneously consider the histogram information of inside and outside the level set contour, to overcome the weak boundary leaking and image noises problem. Experimental results on clinical lumbar vertebrae CT images demonstrate that our proposed approach is more accurate in segmenting with irregular boundaries and more robust to different levels of salt-and-pepper noises.