Automated lesion detection methods for 2D and 3D chest X-ray images

The purpose of this work is to present some technical approaches of our computer-aided detection (CAD) system for chest radiograms and helical CT scans, and also evaluate that by using three databases. The CAD includes some methods to detect lesions and to eliminate false-positive findings. The detection methods consist of template matching and artificial neural network approaches. A genetic algorithm (GA) was employed in template matching to select a matched image from various reference patterns. Artificial neural networks (ANN) were also applied to eliminate the false-positive candidates. The sensitivity and the number of false-positives were 73% and 11 FP per image on chest radiogram CAD and 77% with 2.6 FP per image on helical CT scan CAD. These preliminary results imply that the GA and ANN-based detection methods may be effective in indicating lesions on chest radiograms and helical CT scans.