Automatic Segmentation Method based on Probability Priors and Statistical Shape for Prostate TRUS Images
HUANG Jianbo;NI Dong;WANG Tianfu;National - Regional Key Technology Engineering Laboratory for Medical Ultrasound,Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging,Department of Biomedical Engineering,School of Medicine,Shenzhen University;
To automatically and accurately extract prostate boundary from 2D TRUS images. A novel method of utilizing probability priors and statistical shape for automatic prostate segmentation was presented. First,DENSE SIFT features of image were used to find the location of prostate in image quickly. Next,the optimal model from the multiple mean shape models by using the location was selected. During the segmentation process,missing boundaries in shadow areas were estimated by using the shape model. Last,with the guidance of this shape,the segmentation of an image was executed in a multi- resolution fashion,and an optimal search was performed by minimizing local gray model and local Gaussian distribution energy function for image segmentation. The result showed that the value of average dice similarity coefficient was 0. 9552 and the error of average mean absolute distance was 0. 5016 mm for 30 images. The result demonstrates that the accuracy of this method is obviously improved compared with the traditional Active Shape Model method.