Maximum a posteriori estimation of image boundaries by dynamic programming
Maximum a posteriori estimation of image boundaries by dynamic programming
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DOI：
10.1111/1467-9876.00264
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发表时间：
2002-01-01
影响因子：
1.6
通讯作者：
Young, MJ
中科院分区：
文献类型：
--
作者：
Glasbey, CA;Young, MJ
我们寻求一种计算快速的方法来解决一个困难的图像分割问题：在医学扫描仪图像上定位边界来描绘感兴趣的组织。我们制定了一个贝叶斯模型的图像边界，使最大后验估计是非常有效的动态规划。边界的先验模型是有偏随机游走，似然性基于边界外观模型，参数值从训练图像中获得。该方法被成功地应用于分割的超声图像和X射线计算机断层扫描的羊，在羊育种计划的应用。
We seek a computationally fast method for solving a difficult image segmentation problem: the positioning of boundaries on medical scanner images to delineate tissues of interest. We formulate a Bayesian model for image boundaries such that the maximum a posteriori estimator is obtainable very efficiently by dynamic programming. The prior model for the boundary is a biased random walk and the likelihood is based on a border appearance model, with parameter values obtained from training images. The method is applied successfully to the segmentation of ultrasound images and X-ray computed tomographs of sheep, for application in sheep breeding programmes.