| 摘要 |
<p>An automated method for detection of an object of interest in magnetic resonance (MR) two-dimensional (2-D) images wherein the images comprise gray level patterns, the method includes a learning stage utilizing a set of positive/negative training samples drawn from a specified feature space. The learning stage comprises the steps of estimating the distributions of two probabilities P and N are introduced over the feature space, P being associated with positive samples including said object of interest and N being associated with negative samples not including said object of interest; estimating parameters of Markov chains associated with all possible site permutations using said training samples; computing the best site ordering that maximizes the Kullback distance between P and N ; computing and storing the log-likelihood ratios induced by said site ordering; scanning a test image at different scales with a constant size window; deriving a feature vector from results of said scanning; and classifying said feature vector based on said best site ordering.</p> |
