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OBJECTIVE Accurate classification methods are critical in computer-aided diagnosis (CADx) and other clinical decision support systems. Previous research has reported on methods for combining genetic algorithm (GA) feature selection with ensemble classifier systems in an effort to increase classification accuracy. In this study, we describe a CADx system for(More)
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(More)
2. Improvement of the spatial resolution of the sequence, interpolating gray (or color) values at fractional spatial positions both in the known (transmitted) and the unknown (interpolated) frames. In many video coding schemes, especially at low bitrates, spatial and temporal subsampling of the image sequences is considered. This is realized by leaving out(More)
A video coding scheme is presented in which the coding is performed on individual moving objects. A Markov Random Field model is employed in finding the motion and boundaries of the objects. By guiding the object segmentation process with the spatial color information, meaningful objects representative of the real video scene are extracted. Furthermore,(More)