Localized priors for the precise segmentation of individual vertebras from CT volume data.
We present algorithms for the automatic and precise segmentation of individual vertebras in CT Volume data. When a local surface evolution method such as the level set is applied to such a complex structure, global shape priors will not be sufficient to avoid the leakage and local minima problems, particularly if precise object boundary is desired. We propose a prior knowledge base that contains localized priors--a group of high-level features whose detection will augment the surface model and be the key to success. Base on this a set of context blockers are applied to prevent the leakages. Carefully designed initial surface when registered with the data helps avoid the local minimum problem. The results of segmentation well approximate the human delineated object boundaries. We also present the validation result of the segmentation of 150 vertebras.
Shen
Hong
H
Siemens Corporate Research, Inc., 755 College Road East, Princeton, NJ 08540, USA. shen.hong@siemens.com
Litvin
Andrew
A
Alvino
Christopher
C
D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D013904:Thoracic Vertebrae / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000379:methods*