The goal of the proposed research is to develop computer-aided diagnostic (CAD) schemes for detection and characterization of pulmonary nodules in computed tomography (CT) lung images. The computer output will be used as a "second opinion" to assist radiologists in their interpretation of CT lung nodules for early detection of lung cancer. An advanced CAD scheme will be developed by incorporating a multiple-template matching technique and also a massive training artificial neural network (MTANN) in order to achieve a high sensitivity of 80-90% with a low false positive rate of approximately 0.1 or less per section of low-dose helical CT (LDCT) images. A novel subtraction CT technique will be developed for enhancing subtle lung nodules by suppressing the normal background lung structure including pulmonary vessels. The usefulness of the subtraction CT technique in combination with the conventional CT image will be investigated in improving the overall performance in the computerized detection of lung nodules. In addition to the detection task, a CAD scheme for characterization of nodules will be developed in order to distinguish between benign and malignant lesions. This characterization task will be designed to provide the estimated likelihood of malignancy based on quantitative analysis of image features of nodules detected by computer and/or radiologists. A new psychophysical measure will be determined based on ANN by use of both objective image features and subjective ratings on pairs of similar images, and will be used to select a set of benign and malignant nodules from a large database, which would be similar to an unknown nodule inquestion, in order to assist radiologists' image interpretation. With the high level of detection performance that we expect to achieve, a prototype CAD workstation will be developed and observer performance studies will be carried out to examine the potential usefulness of CAD schemes on detection and classification of pulmonary nodules in CT images. These CAD schemes will provide the radiologists with the location of highly suspected lesions and/or quantitative measures for benign or malignant nodules, and have the potential to improve diagnostic accuracy in the early detection of lung cancer, which may lead to improved prognosis of patients.

R01CA098119

2003-09-19

2009-08-31