Abstract: Computer-aided diagnosis techniques may be combined with techniques to obtain derived images from radiographic images to provide enhanced computer-aided diagnosis of, for example, lung nodules.

Abstract: Computer-aided diagnosis techniques may be combined with techniques to obtain derived images from radiographic images to provide enhanced computer-aided diagnosis of, for example, lung nodules.

Abstract: Computer-aided diagnosis techniques may be combined with dual-energy radiography techniques to provide enhanced computer-aided diagnosis of, for example, lung nodules.

Abstract: Computer-aided diagnosis techniques may be combined with dual-energy radiography techniques to provide enhanced computer-aided diagnosis of, for example, lung nodules.

Abstract: A method of processing radiological images for diagnostic purposes involves the automated registration and comparison of images obtained at different times. A variation on the method may also use computer-aided detection (CAD) in conjunction with image parameters obtained during the process of registration to register CAD results.

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 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 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.

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: Method and system for the detection of interval change in medical images. Three dimensional images, such as previous and current section images in CT scans, are obtained. An anatomic feature, such as the lungs, is used to select sections containing lung by a gray-level thresholding technique. The section correspondence between the current and previous scans is determined automatically. The initial registration of the corresponding sections in the two scans is achieved by a rotation correction and a cross-correlation technique. A more accurate registration between the corresponding current and previous section images is achieved by local matching. A nonlinear warping process which is also based on the cross-correlation technique is applied to the previous image to yield a warped image after the matching. The final subtracted section images were derived by subtracting of the previous section images from the corresponding current section images.

Abstract: Method and system for the detection of interval change in medical images. Three dimensional images, such as previous and current section images in CT scans, are obtained. An anatomic feature, such as the lungs, is used to select sections containing lung by a gray-level thresholding technique. The section correspondence between the current and previous scans is determined automatically. The initial registration of the corresponding sections in the two scans is achieved by a rotation correction and a cross-correlation technique. A more accurate registration between the corresponding current and previous section images is achieved by local matching. A nonlinear warping process which is also based on the cross-correlation technique is applied to the previous image to yield a warped image after the matching. The final subtracted section images were derived by subtracting of the previous section images from the corresponding current section images.

Abstract: A method, system and computer readable medium configured for computerized detection of lung abnormalities, including obtaining a standard digital chest image and a soft-tissue digital chest image; generating a first difference image from the standard digital chest image and a second difference image from the soft-tissue digital chest image; identifying candidate abnormalities in the first and second difference images; extracting from the standard digital chest image and the first difference image predetermined first features of each of the candidate abnormalities identified in the first difference image; extracting from the soft-tissue digital chest image and the second difference images predetermined second features of each of the candidate abnormalities identified in the second difference image; analyzing the extracted first features and the extracted second features to identify and eliminate false positive candidate abnormalities respectively corresponding thereto; applying extracted features from remainin

Abstract: A computer-aided diagnosis (CAD) method for the automated detection of lung nodules in a digital chest image, a computer programmed to implement the method, and a storage medium which stores a program for implementing the method, wherein nodule candidates are first automatically selected by thresholding the difference image and then classified in six groups. A large number of false positives are eliminated by adaptive rule-based tests applied to the original chest image and in the difference image and an artificial neural network (ANN) applied to remaining candidate nodule locations in the original chest image. Using two hundred PA chest radiographs, 100 normal and 100 abnormal, as the database, the presence of nodules in the 100 abnormal cases was confirmed by two experienced radiologists on the basis of CT scans or radiographic follow-up. The CAD method achieves, on average, the sensitivity of 70% at 1.7 false positives per chest image.

Abstract: An automated method, and a computer storage medium storing instructions for executing the method, for analysis of image features in lung nodule detection in a chest radiographic image represented by digital data, including preprocessing the image to identify candidate nodules in the image; establishing a region of interest (ROI) including a candidate nodule identified in the preprocessing step; performing image enhancement of the candidate nodule within the ROI; obtaining a histogram of accumulated edge gradients as a function of radial angles withing the ROI after performing the image enhancement; and determining whether the candidate nodule is a false positive based on the obtained histogram. A 64.times.64-pixel region of interest (ROI) centered at the candidate location is used. The contrast of the ROI is improved by a two-dimensional background subtraction. A nodule shape matched filter is used for enhancement of the nodular pattern located in the central area of the ROI.