Patent ID: 7876943

Claim:
A method for training a classifier for classifying candidate regions in computer aided diagnosis of digital medical images, said method comprising the steps of: providing a training set of images, each said image including one or more candidate regions that have been identified as suspicious by a candidate generation step of a computer aided diagnosis system, and wherein each said image has been manually annotated to identify lesions; deriving a set of descriptive feature vectors from a feature computation step of a computer aided diagnosis system, wherein each candidate region is associated with a feature vector, wherein a subset of said features are conditionally dependent, and the remaining features are conditionally independent; using said conditionally independent features to train a naïve Bayes classifier that classifies said candidate regions as lesion or non-lesion; determining a joint probability distribution from said training images that models the conditionally dependent features; determining from said training images a prior-odds probability ratio of a candidate region being associated with a lesion; and forming a new classifier from a product of said naïve Bayes classifier, said joint probability distribution for the conditionally dependent features, and said prior-odds probability ratio.