Novel use of the Hotelling observer for computer-aided diagnosis of solitary pulmonary nodules

We propose to investigate a novel use of the Hotelling observer for the task of discrimination of solitary pulmonary nodules from a database of regions that were all deemed suspicious. A database of 239 regions of interest (ROIs) was collected from digitized chest radiographs. Each of these 256x256 pixel ROIs contained a suspicious lesion in the center for which we have a truth file. For our study, 25 separate Hotelling observers were set up in a 5x5 grid across the center of the ROIs. Each separate observer was designed to 'observe' a 15x15 pixel area of the image. Leave-one-out training was used to generate 25 output observer features. These 25 features were then narrowed down using a sequential forward searching linear discriminant analysis. The forward search was continued until the accuracy declined at 13 features and the subset was used as the input layer to an artificial neural network (ANN). This network was trained to minimize mean squared error and the output was the area under the ROC curve. The trained ANN gave an ROC area of .86. In comparison, three radiologists performed at ROC area indexes of .72, .79, and .83.