An Improved Confidence Connected Liver Segmentation Method Based on Three Views of CT Images

Abstract

To enhance the accuracy of liver segmentation, we present an improved confidence connected liver segmentation method, which combines the liver segmentation results obtained from three views is proposed. First, to reduce noise, an improved curvature anisotropic diffusion filter is applied, which simultaneously stores edge information. Second, seed points located in the liver are selected automatically using statistics and analysis of the liver intensity. We extract the liver contours from three views of computed tomography (CT) images using the confidence connected method and improve the contours by the cavity filling method. Finally, we combine the liver contours extracted from the coronal, sagittal, and cross section. In our experiments, clinical validation is performed using ten abdominal CT datasets. The results show that our proposed method can extract liver contours quickly and accurately, achieving an overall true positive rate (TPR) of 0.97. In addition, this method is useful for clinical diagnosis of liver disorders and virtual surgical planning.