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A novel approach for automatic and robust segmentation of the 3D liver in computed tomography images

Liver segmentation is important for the computer-assisted diagnosis of liver disease and liver volume measurement. Because of the complexity of liver computed tomography (CT) images, multiple image segmentation techniques are required to robustly segment the liver. Many liver segmentation approaches have been proposed in the past few decades, however the liver segmentation problem remains. More robust approaches are needed and new possibilities should be investigated. In this paper, we propose a novel 3D liver segmentation approach that contains four major parts: (1) image pre-processing, (2) edge-constrained liver segmentation by 3D region growing, (3) separation of 3D liver and other organs, and (4) post-processing. In the first step, an anisotropic diffusion filter removes noise effectively while keeping the image edges almost unchanged. In the second step, both 3D and 2D canny edges are combined to constrain the region growing process. The seeds of region growing are generated by slope difference distribution threshold selection of the 3D CT image and morphological erosion. In the third step, 3D random walk and slope difference distribution threshold selection are combined to separate the adhered organs. In the fourth step, a novel 2D vascular region correction method is proposed for post-processing. Two open accessible CT liver datasets are used to evaluate the proposed approach and the achieved average DICE measure accuracy is 96.83%.