Segmentation of kidney from ultrasound B-mode images with texture-based classification.
The segmentation of anatomical structures from sonograms can help physicians evaluate organ morphology and realize quantitative measurement. It is an important but difficult issue in medical image analysis. In this paper, we propose a new method based on Laws' microtexture energies and maximum a posteriori (MAP) estimation to construct a probabilistic deformable model for kidney segmentation. First, using texture image features and MAP estimation, we classify each image pixel as inside or outside the boundary. Then, we design a deformable model to locate the actual boundary and maintain the smooth nature of the organ. Using gradient information subject to a smoothness constraint, the optimal contour is obtained by the dynamic programming technique. Experiments on different datasets are described. We find this method to be an effective approach.
Wu
Chia-Hsiang
CH
Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan, ROC.
Sun
Yung-Nien
YN
D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D007668:Kidney / Q000000981:diagnostic imaging*; D008954:Models, Biological; D014463:Ultrasonography