Abstract: A computerized method and system for the radiographic analysis of bone structure and risk of future fracture with or without the measurement of bone mass. Techniques including texture analysis for use in quantitating the bone structure and risk of future fracture. The texture analysis of the bone structure incorporates directionality information, for example in terms of the angular dependence of the RMS variation and first moment of the power spectrum of a ROI in the bony region of interest. The system also includes using dual energy imaging in order to obtain measures of both bone mass and bone structure with one exam. Specific applications are given for the analysis of regions within the vertebral bodies on conventional spine radiographs. Techniques include novel features that characterize the power spectrum of the bone structure and allow extraction of directionality features with which to characterize the spatial distribution and thickness of the bone trabeculae.

Abstract: A method and system for the automated detection of lesions in medical images. Medical images, such as mammograms are segmented and optionally processing with peripheral enhancement and/or modified median filtering. A modified morphological open operation and filtering with a modified mass filter are performed for the initial detection of circumscribed lesions. Then, the lesions are matched using a deformable shape template with Fourier descriptors. Characterization of the match is done using simulated annealing, and measuring the circularity and density characteristics of the suspected lesion. The procedure is performed iteratively at different spatial resolution in which at each resolution step a specific lesion size is detected. The detection of the lesion leads to a localization of a suspicious region and thus the likelihood of cancer.

Abstract: A method for the automated segmentation of an abnormality in a medical image, including acquiring first image data representative of the medical image; locating a suspicious site at which the abnormality may exist; establishing a seed point within the suspicious site; and preprocessing the suspicious site with a constraint function to produce second image data in which pixel values distant of the seed point are suppressed. Preprocessing includes using an isotropic Gaussian function centered on the seed point as the constraint function, or for example using an isotropic three dimensional Gaussian function centered on the seed point as the constraint function.

Abstract: A method, computer program product, and system for assessing tumor extent in medical temporally acquired images. The method, on which the computer program product and system is based, includes obtaining image data corresponding to temporally acquired images including a tumor and surrounding anatomy and performing variance processing on the obtained image data to derive variance image data defining a variance image indicative of variation of voxels in the temporally acquired medical images. Techniques include novel developments and implementations of breast volume segmentation, breast border removal, lesion enhancement, determination of the bounding sphere, computation of a 3-D search volume, suppression of surrounding structures, and volume growing. Output from the methods yields an estimate of the extent of the tumor (lesion) in the breast.

Abstract: A method and apparatus for the computerized automatic analysis of lesions in ultrasound images, including the computerized analysis of lesions in the breast, using gradient, gray-level, and texture based measures. Echogenicity features are developed to assess the characteristics of the lesions and in some cases give an estimate of the likelihood of malignancy or of prognosis. The output from the computerized analysis is used in making a diagnosis and/or prognosis. For example, with the analysis of the ultrasound images of the breast, the features can be used to either distinguish between malignant and benign lesions, or distinguish between (i.e., diagnosis) the types of benign lesions such as benign solid lesions (e.g., fibroadenoma), simple cysts, complex cysts, and benign cysts. The ultrasound image features can be merged with those from mammographic and/or magnetic resonance images of the same lesion for classification by means of a common artificial neural network.

Abstract: A method and system for the computerized registration of radionuclide images with radiographic images, including generating image data from radiographic and radionuclide images of the thorax. Techniques include contouring the lung regions in each type of chest image, scaling and registration of the contours based on location of lung apices, and superimposition after appropriate shifting of the images. Specific applications are given for the automated registration of radionuclide lungs scans with chest radiographs. The method in the example given yields a system that spatially registers and correlates digitized chest radiographs with V/Q scans in order to correlate V/Q functional information with the greater structural detail of chest radiographs. Final output could be the computer-determined contours from each type of image superimposed on any of the original images, or superimposition of the radionuclide image data, which contains high activity, onto the radiographic chest image.

Abstract: A computerized method and system for the radiographic analysis of bone structure and risk of future fracture with or without the measurement of bone mass. Techniques including texture analysis for use in quantitating the bone structure and risk of future fracture. The texture analysis of the bone structure incorporates directionality information, for example in terms of the angular dependence of the RMS variation and first moment of the power spectrum of a ROI in the bony region of interest. The system also includes using dual energy imaging in order to obtain measures of both bone mass and bone structure with one exam. Specific applications are given for the analysis of regions within the vertebral bodies on conventional spine radiographs. Techniques include novel features that characterize the power spectrum of the bone structure and allow extraction of directionality features with which to characterize the spatial distribution and thickness of the bone trabeculae.

Abstract: A method and system for the automated detection of lesions in computed tomographic images, including generating image data from at least one selected portion of an object, for example, from CT images of the thorax. The image data are then analyzed in order to produce the boundary of the thorax. The image data within the thoracic boundary is then further analyzed to produce boundaries of the lung regions using predetermined criteria. Features within the lung regions are then extracted using multi-gray-level thresholding and correlation between resulting multi-level threshold images and between at least adjacent sections. Classification of the features as abnormal lesions or normal anatomic features is then performed using geometric features yielding a likelihood of being an abnormal lesion along with its location in either the 2-D image section or in the 3-D space of the object.

Abstract: A method and system for the automated detection and classification of masses in mammograms. These method and system include the performance of iterative, multi-level gray level thresholding, followed by a lesion extraction and feature extraction techniques for classifying true masses from false-positive masses and malignant masses from benign masses. The method and system provide improvements in the detection of masses include multi-gray-level thresholding of the processed images to increase sensitivity and accurate region growing and feature analysis to increase specificity. Novel improvements in the classification of masses include a cumulative edge gradient orientation histogram analysis relative to the radial angle of the pixels in question; i.e., either around the margin of the mass or within or around the mass in question. The classification of the mass leads to a likelihood of malignancy.

Abstract: A method and system for the automated detection of lesions such as masses and/or tissue (parenchymal) distortions in medical images such as mammograms. Dense regions and subcutaneous fat regions within a mammogram are segmented. A background correction may be performed within the dense regions. Hough spectrum within ROIs placed in the breast region of a mammogram are calculated and thresholded using the intensity value .eta. in order to increase sensitivity and reduce the number of false-positive detections. Lesions are detected based on the thresholded Hough spectra. The thresholded Hough spectra are also used to differentiate between benign and malignant masses.

Abstract: A method for the automated detection of gross abnormalities and asymmetries in chest images, including generating image data from radiographic images of the thorax. The image data are then analyzed in order to produce the boundaries of the aerated lung regions in the thorax. This analysis comprises location of the mediastinum and lung apices, iterative global thresholding with centroid testing of contours, local thresholding on regions along initial contours of the aerated lung, correction for regions near the costo- and cardiophrenic angles in the chest, analysis of the areas and density distribution within the aerated lung regions in the chest and determination of the likelihood of the presence of a gross abnormality or asymmetry. Final output could be the computer determined contours of the lungs or the likelihood for abnormality.

Abstract: A method of computerized detection of clustered microcalcifications in digital mammograms, including obtaining a digitized mammogram, deriving a difference image signal from the digitized mammogram, performing global grey-level thresholding, area filtering, and local grey-level thresholding on the difference image, in that order, performing a texture discrimination of the signal extracted from the previous step, performing a cluster filtering technique on the texture discriminated signals to identify locations in the digitized mammogram corresponding to candidate clustered microcalcifications, performing a feature extraction step in which the area, contrast and background pixel values of signals corresponding to the candidate clustered microcalcifications in the original image are extracted, performing thresholding tests based on the extracted features to eliminate from the candidate clustered microcalcifications particular candidate clustered microcalcification identified as corresponding to false-positive i

Abstract: A method for the automated segmentation of medical images, including generating image data from radiographic images of the breast. The method is applicable to breast mammograms including the extraction of the skinline as well as correction for non-uniform exposure conditions, hand radiographs, and chest radiographs. Techniques for the segmentation include noise filtering, local gray value range determination, modified global histogram analysis, region growing and determination of object contour. The method also is applicable to skin detection and analysis of skin thickening in medical images, where image segmentation, local optimization of external skinline, creation of a gradient image, identification of the internal skinline and then skin thickness determination are carried out.

Abstract: A computerized method and system for reducing the number of false-positive detections of nodule candidates in the detection of abnormalities in digital chest radiography. The image is initially subjected to an image difference technique where the detection sensitivity is increased so as to avoid missing small nodules which might otherwise go undetected. Such a technique tends to increase the number of false-positives, however, leading to possible incorrect diagnoses of the radiographs. To reduce the number of false-positives, feature extraction techniques are applied to grown regions around the nodule candidates, in order to provide computer generated information concerning the candidates. A data base of parameters common to false-positives is compared to calculated parameters of a candidate of interest. The candidates with grown region parameters within the data base range common to false-positives are eliminated as being probable false-positive detections due to normal background anatomical features.

Abstract: A method for automated analysis of abnormalities in the form of lesions and parenchymal distortions using digital images, including generating image data from respective of digital images derived from at least one selected portion of an object, for example, from mammographical digital images of the left and right breasts. The image data from each of the digital images are then correlated to produce correlated data in which normal anatomical structured background is removed. The correlated data is then searched using one or more predetermined criteria to identify in at least one of the digital images an abnormal region represented by a portion of the correlated data which meets the predetermined criteria. The location of the abnormal region is then indicated, and the indicated location is then subjected to classification processing to determine whether or not the abnormal region is benign or malignant. Classification is performed based on the degree of spiculations of the identified abnormal region.

Abstract: A method and system for detecting and displaying abnormal anatomic regions existing in a digital X-ray image, wherein a single projection digital X-ray image is processed to obtain signal-enhanced image data with a maximum signal-to-noise ratio (SNR) and is also processed to obtain signal-suppressed image data with a suppressed SNR. Then, difference image data are formed by subtraction of the signal-suppressed image data from the signal-enhanced image data to remove low-frequency structured anatomic background, which is basically the same in both the signal-suppressed and signal-enhanced image data. Once the structured background is removed, feature extraction, is performed. For the detection of lung nodules, pixel thresholding is performed, followed by circularity and/or size testing of contiguous pixels surviving thresholding. Threshold levels are varied, and the effect of varying the threshold on circularity and size is used to detect nodules.