IEEE Transactions on Pattern Analysis and Machine Intelligence
Direct Least Square Fitting of Ellipses
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
Computer Methods and Programs in Biomedicine
Computerized segmentation of whole-body bone scintigrams and its use in automated diagnostics
Computer Methods and Programs in Biomedicine
Image processing for combined bright-field and reflection interference contrast video microscopy
Computer Methods and Programs in Biomedicine
Model-based quantitative AAA image analysis using a priori knowledge
Computer Methods and Programs in Biomedicine
Cytological image analysis with a genetic fuzzy finite state machine
Computer Methods and Programs in Biomedicine
Editorial: Medical image segmentation: Quo Vadis
Computer Methods and Programs in Biomedicine
Expert Systems with Applications: An International Journal
Automatic ultrasound kidney's centroid detection system
Proceedings of the 15th WSEAS international conference on Computers
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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.