Organ Segmentation from 3D Abdominal CT Images Based on Atlas Selection and Graph Cut

This paper presents a method for segmenting abdominal organs from 3D abdominal CT images based on atlas selection and graph cut. The training samples are divided into multiple clusters based on the image similarity. The average image and atlas for each cluster are created. For an input image, we select the most similar atlas to the input image by measuring the image similarity between the input and average images. Segmentation of organs based on the MAP estimation using the selected atlas is then performed, followed by the precise segmentation by the graph cut algorithm. We applied the proposed method to a hundred cases of CT images. The experimental results showed that the extraction accuracy could be improved using multiple atlases, achieving more than 90% of the precision rate except for the pancreas.

[1]  Daniel Rueckert,et al.  Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy , 2009, NeuroImage.

[2]  Yoshinobu Sato,et al.  Construction of Hierarchical Multi-Organ Statistical Atlases and Their Application to Multi-Organ Segmentation from CT Images , 2008, MICCAI.

[3]  Nassir Navab,et al.  Dense image registration through MRFs and efficient linear programming , 2008, Medical Image Anal..

[4]  Marie-Pierre Jolly,et al.  Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[5]  Jitendra Malik,et al.  Normalized Cuts and Image Segmentation , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Gabor Fichtinger,et al.  Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008, 11th International Conference, New York, NY, USA, September 6-10, 2008, Proceedings, Part I , 2008, International Conference on Medical Image Computing and Computer-Assisted Intervention.

[7]  Olga Veksler,et al.  Fast Approximate Energy Minimization via Graph Cuts , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[9]  Jean Meunier,et al.  Average Brain Models: A Convergence Study , 2000, Comput. Vis. Image Underst..

[10]  Akinobu Shimizu,et al.  Automated pancreas segmentation from three-dimensional contrast-enhanced computed tomography , 2009, International Journal of Computer Assisted Radiology and Surgery.

[11]  Hyunjin Park,et al.  Construction of an abdominal probabilistic atlas and its application in segmentation , 2003, IEEE Transactions on Medical Imaging.