Feature subset selection for improving the performance of false positive reduction in lung nodule CAD.

We propose a feature subset selection method based on genetic algorithms to improve the performance of false positive reduction in lung nodule computer-aided detection (CAD). It is coupled with a classifier based on support vector machines. The proposed approach determines automatically the optimal size of the feature set, and chooses the most relevant features from a feature pool. Its performance was tested using a lung nodule database (52 true nodules and 443 false ones) acquired by multislice CT scans. From 23 features calculated for each detected structure, the suggested method determined ten to be the optimal feature subset size, and selected the most relevant ten features. A support vector machine classifier trained with the optimal feature subset resulted in 100% sensitivity and 56.4% specificity using an independent validation set. Experiments show significant improvement achieved by a system incorporating the proposed method over a system without it. This approach can be also applied to other machine learning problems; e.g. computer-aided diagnosis of lung nodules.

Generating XML schemas for DICOM structured reporting templates.

In this paper, the authors describe a methodology to transform programmatically structured reporting (SR) templates defined by the Digital Imaging and Communications for Medicine (DICOM) standard into an XML schema representation. Such schemas can be used in the creation and validation of XML-encoded SR documents that use templates. Templates are a means to put additional constraints on an SR document to promote common formats for specific reporting applications or domains. As the use of templates becomes more widespread in the production of SR documents, it is important to ensure validity of such documents. The work described in this paper is an extension of the authors' previous work on XML schema representation for DICOM SR. Therefore, this paper inherits and partially modifies the structure defined in the earlier work.