Abstract: A method and system for efficiently identifying materialized view candidates for queries filters materialized views using certain criteria, using the materialized view signatures. This filtering rejects some of the unqualified materialized views prior to the performance of the query rewrite matching algorithm, resulting in a group of materialized view candidates. The query rewrite matching algorithm is then performed on the materialized view candidates. By first filtering the materialized views based on their signatures, the number of materialized views on which the query rewrite matching algorithm is performed is significantly reduced, improving performance.

Abstract: A fuzzy logic based classification (FLBC) method for the automated discrimination of objects and the automated identification of nodules based on their features, a computer programmed to implement the method, and a storage medium which stores a program for implementing the method, wherein nodule (or, object) features are first normalized and then automatically selected. Based on the selected features, suspect nodules (or, objects) are pre-grouped and then subjected to the corresponding trained linear classifier to remove those false positive nodules or abnormal objects that are linearly separable. Finally, the remaining suspect nodules or objects are further subjected to a trained fuzzy classifier for removing those false positive nodules or abnormal objects that are not linearly separable.

Abstract: A divide-and-conquer (DAC) method and system improve the detection of abnormalities, like lung nodules, in radiological images via the use of zone-based digital image processing and artificial neural networks. The DAC method and system divide the lung zone into different zones in order to enhance the efficiency in detection. Different image enhancement techniques are used for each different zone to enhance nodule images, as are different zone-specific techniques for selecting suspected abnormalities, extracting image features corresponding to selected abnormalities, and classifying the abnormalities as either true or false abnormalities.

Abstract: A method of detecting and analyzing abnormalities, like lung nodules, in thoracic computer tomography (CT) images uses digital image processing techniques and adaptive computing methods. The techniques include an automatic detection process to detect candidate abnormalities, an image matching process to match CT slices from two different CT scans, and a measurement process that determines parameters of the candidate abnormalities. Final results and processed CT images are displayed on a user interface.