Abstract: A user interface for facilitating a computer-aided diagnosis (CAD) includes an image input device that receives and scans the multiple images, and a film feeder that transports films from a film developer to the scanner. A multi-image identification input device uses bar code reading, numeric keypad, keyboard, network, and/or mouse to enter multiple image identification prior to image scanning. A display includes an image identification area, case processing status area, and a CAD detection and user modification area. The user can edit CAD results—accept, remove—and can add new diagnosis results to create a new composite diagnosis result.

Abstract: A method for identifying the orientation of an interesting object in a digital medical image comprises steps of creating a rectangular interesting image mask that covers the interesting object, based on the original digital medical image; generating a rough image based on the interesting image mask, the rough image coarsely describing the interesting object; and identifying the orientation of the interesting object based on the rough image. A method for segmenting interesting objects in digital medical images may also comprise steps of creating a rectangular interesting image mask that covers said interesting object, based on an original digital medical image; generating a rough image based on the interesting image mask, the rough image coarsely describing the interesting object; and performing a post-process on the rough image.

Abstract: A method of processing x-ray images in digital form comprises: (a) inputting an x-ray image in digital form; (b) determining one or more normalization factors based on the pixels of the input x-ray image; (c) performing normalization on the input x-ray image by applying the one or more normalization factors to the pixels; and (d) outputting a normalized digital x-ray image.

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.

Abstract: A method and system improve the detection of abnormalities, such as lung nodules, in radiological images using digital image processing and artificial neural network techniques. The detection method and system use a nodule phantom for matching in order to enhance the efficiency in detection. The detection method and system use spherical parameters to characterize true nodules, thus enabling detection of the nodules in the mediastinum. The detection method and system use a multi-layer back-propagation neural network architecture not only for the classification of lung nodules but also for the integration of detection results from different classifiers. In addition, this method and system improve the detection efficiency by recommending the ranking of true nodules and several false positive nodules prior to the training of the neural network classifier. The method and system use image segmentation to remove regions outside the chest in order to reduce the false positives outside the chest region.

Abstract: A user interface for facilitating a computer-aided diagnosis (CAD) includes an image input device that receives and scans the multiple images, and a film feeder that transports films from a film developer to the scanner. A multi-image identification input device uses bar code reading, numeric keypad, keyboard, network, and/or mouse to enter multiple image identification prior to image scanning. A display includes an image identification area, case processing status area, and a CAD detection and user modification area. The user can edit CAD results—accept, remove—and can add new diagnosis results to create a new composite diagnosis result.

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.