Non-parametric Iterative Model Constraint Graph min-cut for Automatic Kidney Segmentation
We present a new non-parametric model constraint graph min-cut algorithm for automatic kidney segmentation in CT images. The segmentation is formulated as a maximum a-posteriori estimation of a model-driven Markov random field. A non-parametric hybrid shape and intensity model is treated as a latent variable in the energy functional. The latent model and labeling map that minimize the energy functional are then simultaneously computed with an expectation maximization approach. The main advantages of our method are that it does not assume a fixed parametric prior model, which is subjective to inter-patient variability and registration errors, and that it combines both the model and the image information into a unified graph min-cut based segmentation framework. We evaluated our method on 20 kidneys from 10 CT datasets with and without contrast agent for which ground-truth segmentations were generated by averaging three manual segmentations. Our method yields an average volumetric overlap error of 10.95％, and average symmetric surface distance of 0.79mm. These results indicate that our method is accurate and robust for kidney segmentation.
M.Freiman A.Kronman S.J.Esses L.Joskowicz J.Sosna
School of Eng.and Computer Science, The Hebrew Univ.of Jerusalem, Israel Mount Sinai School of Medicine, New York, NY Dept.of Radiology, Hadassah Hebrew University Medical C Dept.of Radiology, Hadassah Hebrew University Medical Center, Jerusalem, Israel
国际会议
北京
英文
73–80
2010-09-01（万方平台首次上网日期，不代表论文的发表时间）