Automated segmentation of the liver from 3D CT images using probabilistic atlas and multi-level statistical shape model.
An atlas-based automated liver segmentation method from 3D CT images is described. The method utilizes two types of atlases, that is, the probabilistic atlas (PA) and statistical shape model (SSM). Voxel-based segmentation with PA is firstly performed to obtain a liver region, and then the obtained region is used as the initial region for subsequent SSM fitting to 3D CT images. To improve reconstruction accuracy especially for largely deformed livers, we utilize a multi-level SSM (ML-SSM). In ML-SSM, the whole shape is divided into patches, and principal component analysis is applied to each patches. To avoid the inconsistency among patches, we introduce a new constraint called the adhesiveness constraint for overlap regions among patches. In experiments, we demonstrate that segmentation accuracy improved by using the initial region obtained with PA and the introduced constraint for ML-SSM.
Okada
Toshiyuki
T
Division of Image Analysis, Osaka University Graduate School of Medicine, Suita, Osaka 565-0871, Japan. toshi@image.med.osaka-u.ac.jp
Shimada
Ryuji
R
Sato
Yoshinobu
Y
Hori
Masatoshi
M
Yokota
Keita
K
Nakamoto
Masahiko
M
Chen
Yen-Wei
YW
Nakamura
Hironobu
H
Tamura
Shinichi
S
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008099:Liver / Q000000981:diagnostic imaging*; D008107:Liver Diseases / Q000000981:diagnostic imaging*; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D011860:Radiography, Abdominal / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*