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Abstract

A method and system improve the detection of abnormalities, such as lung nodules, in radiological images using digital image processing and artificial neural network techniques. The detection method and system use a nodule phantom for matching in order to enhance the efficiency in detection. The detection method and system use spherical parameters to characterize true nodules, thus enabling detection of the nodules in the mediastinum. The detection method and system use a multi-layer back-propagation neural network architecture not only for the classification of lung nodules but also for the integration of detection results from different classifiers. In addition, this method and system improve the detection efficiency by recommending the ranking of true nodules and several false positive nodules prior to the training of the neural network classifier. The method and system use image segmentation to remove regions outside the chest in order to reduce the false positives outside the chest region.

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