System and method for lesion detection using locally adjustable priors
US7876943B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 30, 2008 |
| Grant date | Jan 25, 2011 |
| Priority date | — |
| Expiry date | Aug 13, 2029 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V2201/032
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
According to an aspect of the invention, a method for training a classifier for classifying candidate regions in computer aided diagnosis of digital medical images includes providing a training set of annotated images, each image including one or more candidate regions that have been identified as suspicious, deriving a set of descriptive feature vectors, where each candidate region is associated with a feature vector. A subset of the features are conditionally dependent, and the remaining features are conditionally independent. The conditionally independent features are used to train a naïve Bayes classifier that classifies the candidate regions as lesion or non-lesion. A joint probability distribution that models the conditionally dependent features, and a prior-odds probability ratio of a candidate region being associated with a lesion are determined from the training images. A new classifier is formed from the naïve Bayes classifier, the joint probability distribution, and the prior-odds probability ratio.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.