No abstract

This system was developed to provide a reproducible method that could be used to evaluate the lobar location, severity, and extent of a broad spectrum of CT features of CF lung disease, especially in children. This study demonstrates that the overall score is both sensitive to variation in the severity of lung disease and reproducible.

Abstract-This paper describes a new hierarchical approach to content-based image retrieval called the "customized-queries" approach (CQA). Contrary to the single feature vector approach which tries to classify the query and retrieve similar images in one step, CQA uses multiple feature sets and a two-step approach to retrieval. The first step classifies the query according to the class labels of the images using the features that best discriminate the classes. The second step then retrieves the most similar images within the predicted class using the features customized to distinguish "subclasses" within that class. Needing to find the customized feature subset for each class led us to investigate feature selection for unsupervised learning. As a result, we developed a new algorithm called FSSEM (feature subset selection using expectation-maximization clustering). We applied our approach to a database of high resolution computed tomography lung images and show that CQA radically improves the retrieval precision over the single feature vector approach. To determine whether our CBIR system is helpful to physicians, we conducted an evaluation trial with eight radiologists. The results show that our system using CQA retrieval doubled the doctors' diagnostic accuracy.

No abstract

A software system and database for computer-aided diagnosis with thin-section computed tomographic (CT) images of the chest was designed and implemented. When presented with an unknown query image, the system uses pattern recognition to retrieve visually similar images with known diagnoses from the database. A preliminary validation trial was conducted with 11 volunteers who were asked to select the best diagnosis for a series of test images, with and without software assistance. The percentage of correct answers increased from 29% to 62% with computer assistance. This finding suggests that this system may be useful for computer-assisted diagnosis.