The goal of the proposed research is to develop computer-aided diagnostic (CAD) schemes for detection of lung nodules, interstitial infiltrates, cardiomegaly and pneumothoraces in digital chest images. We plan to develop advanced computerized schemes and software for improvements in sensitivities, specificities and efficiencies in order to implement and evaluate such schemes in a controlled clinical environment. We believe that these computer-aided diagnostic schemes, which provide the radiologist with the location and/or quantitative measures of highly suspected lesions, have the potential to improve diagnostic accuracy in the detection of cancer by reducing human errors associated with radiologic diagnosis. Specifically, we plan to (1) develop an improved scheme for automated detection of lung nodules by (a) combinations of linear and nonlinear morphological filtering techniques based on a difference image method for enhancement and suppression of lung nodules, (b) reduction of false positive detections by detailed analysis of image features by chest radiologists and also use of artificial neural networks, and (c) observer performance studies for optimal use of CAD methods; (2) develop an improved scheme for automated lung texture analysis by (a) devising an automated technique for sampling numerous regions of interest (R01s) in lung fields, (b) investigation of new texture measures based on analysis of the shape and anisotropic properties of the power spectrum of lung textures, and (c) application of artificial neural networks for detection and classification of interstitial infiltrates; (3) develop an improved scheme for automated analysis of sizes of heart and lung by (a) employing an iterative fitting technique on cardiac contours, using a shift-variant cosine function as a model for the cardiac contour, and histogram analysis, and (b) comparison of radiologists' performances with and without the CAD scheme by means of receiver operating characteristic (ROC) analysis; (4) develop an automated scheme for detection of pneumothorax by (a) application of the Hough transform in conjunction with an edge enhancement technique for detection of subtle curved line, and (b) ROC analysis of radiologists' performances for evaluation of the usefulness of the CAD scheme; and (5) implement and evaluate the CAD schemes i a high-resolution, high-speed image processing system by (a) development of a prototype intelligent workstation with efficient algorithms and efficient man-machine interfaces, and (b) carrying out pilot studies on clinical evaluation of our chest CAD schemes in comparison with conventional readings in terms of the four types of abnormalities related to lung nodules, interstitial infiltrates, cardiomegaly and pneumothoraces.
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