An intensity statistics based graph cut segmentation algorithm is proposed in this paper to improve the accuracy and adaptive capacity of liver segmentation. The proposed segmentation method consists of four steps as follows: First, combined with the Otsu algorithm and associated with a cropped liver image, we defined a gray interval as the livers intensity range. Second, the fuzzy c-means clustering algorithm was applied to compute the average intensity and the variance. Third, we establish the cost function with the statistic results. Finally, we employed the improved graph cut model to extract the liver parenchyma from a large cross-section liver image. Experimental results show that the proposed segmentation method is feasible for different liver images of different intensity statistics.
Intra- and inter-observer reproducibility of volume measurement of knee cartilage segmented from the OAI MR image set using a novel semi-automated segmentation method