Segmentation of prostate from 3-D ultrasound volumes using shape and intensity priors in level set framework.
This paper presents a fully automatic prostate segmentation system in transrectal ultrasound images based on 3-D shape and intensity priors. 2-D manual segmentations from training image data are stacked to create the coarse 3-D shape. Min/Max flow is used to transform each coarse shape into smooth 3-D surface. Principle component analysis method is utilized to extract the 3-D shape mode from the training data sets. In a Bayesian inference, the nonlinear shape model is integrated with a nonparametric intensity prior and define a region based energy function. The energy is minimized in a level set frameworks and the control parameters of the convergence lead to the final segmentation. The developed method was tested on 3-D transrectal ultrasound images and its performance compared with manually-defined ground truth. The correct segmentation rate is 0.82.
Yang
Fuxing
F
Diagnostic Ultrasound, Bothell, WA 98021, USA. fuxing.yang@gmail.com
Suri
Jasjit
J
Fenster
Aaron
A
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008297:Male; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*; D011467:Prostate / Q000000981:diagnostic imaging*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014463:Ultrasonography / Q000379:methods*