Abstract: A method and apparatus for analyzing a medical image obtained from one of a plurality of digital modalities, the method comprising transforming or mapping the initial medical image to create a uniform contrast response and appearance regardless of the original modality of the image.

Abstract: A method and apparatus for analyzing a medical image obtained from one of a plurality of modalities, the method comprising normalizing the medical image to create a uniform display quality regardless of the original modality of the image.

Abstract: A computer-aided diagnostic method and system provide image annotation information that can include an assessment of the probability, likelihood or predictive value of detected or identified suspected abnormalities as an additional aid to the radiologist. More specifically, probability values, in numerical form and/or analog form, are added to the locational markers of the detected and suspected abnormalities. The task of a physician using such a system as disclosed is believed to be made easier by displaying markers representing different regions of interest.

Abstract: Simultaneous grayscale and geometric registration of images, such as mammograms, facilitates temporal comparison and enhances the speed and reliability of computer aided diagnosis (CAD) detection of medical abnormalities. The method generally includes optimizing a merit function, e.g., sum of squared errors, containing parameters associated with a transformation function for simultaneous geometric and grayscale registering of the images, the optimizing of the merit function being performed by determining optimal values of the parameters using data in the images and registering one image to the other by applying the geometric and grayscale transformation function using the optimal values of the parameters. The optimizing may be performed iteratively from coarse to fine resolutions using a modified Levenberg-Marquardt method for optimizing nonlinear parameters with linear regression for optimizing linear parameters.

Abstract: An algorithm is quickly scans a digital image volume to detect density nodules. A first stage is based on a transform to quickly highlight regions requiring further processing. The first stage operates with somewhat lower sensitivity than is possible with more detailed analyses, but operates to highlight regions for further analysis and processing. The transform dynamically adapts to various nodule sizes through the use of radial zones. A second stage uses a detailed gradient distribution analysis that only operates on voxels that pass a threshold of the first stage.

Abstract: A method, system, and method of doing business in a standards-based medical imaging environment is described in relation to the communication of computer-aided detection (CAD) results among devices, with one embodiment relating to the DICOM standard. CAD results are fixably integrated into the pixels of a secondary image derived from a source image, and the secondary image is transferred using a DICOM Secondary Capture Image Information Object Instance (SCI-IOI). The DICOM SCI-IOI is transferred to a viewing workstation, whereby a clinician can open, manipulate, and view the secondary image in a side-by-side comparison with the source image or derivative images thereof. Advantageously, CAD results are communicated to the clinician in a DICOM-conforming manner independent of whether the viewing workstation supports standard DICOM accommodations, such as CAD Structured Report Information Object Instances (CAD SR-IOIs), for the type of CAD results that need to be presented.

Abstract: The invention provides a hierarchical method that is based on multi-scale computation and motion-tracking algorithms to register images. A hierarchical registration algorithm is used to address extrinsic and intrinsic variations. In particular, a global similarity transformation is applied to align anatomical structures in a global manner. Next, a local similarity transformation is applied to fine-tune and adjust internal details. The registration output provides the input necessary for other automated computations, such as volume and size comparisons and measurements.

Abstract: Numerical image processing of two or more medical images to provide grayscale registration thereof is described, the numerical image processing algorithms being based at least in part on a model of medical image acquisition. The grayscale registered temporal images may then be displayed for visual comparison by a clinician and/or further processed by a computer-aided diagnosis (CAD) system for detection of medical abnormalities therein. A parametric method includes spatially registering two images and performing gray scale registration of the images. A parametric transform model, e.g., analog to analog, digital to digital, analog to digital, or digital to analog model, is selected based on the image acquisition method(s) of the images, i.e., digital or analog/film.

Abstract: An algorithm is disclosed that recovers regions of possible pleural nodules left out of an organ field or otherwise undetected due to the nature of low level image processing in the organ field. A morphological closing with an elliptical structuring element is performed on a region to detect nodules within the size of the ellipsoid. A deformable surface-based analysis is performed in distinctive regions for the identification of larger nodules. The integrated use of a deformable surface model and chamfer distance potential enables explicit representation of regularized, or smoothed, surfaces within which nodule candidates may be detected.

Abstract: A method and apparatus for the fast detection of spiculated lesions in a digital mammogram, the method for use in a computer aided diagnosis system for assisting a radiologist in identifying and recognizing the spiculations among a multiplicity of lines corresponding to standard fibrous breast tissue. A line and direction image is created from a digital mammogram, and a region of potential intersection for substantially every pixel in the digital mammogram image is determined. The region of potential intersection for each pixel is a predetermined pattern, such as a high aspect ratio rectangle or trapezoid, positioned around the pixel and rotated in a direction corresponding to direction information for that pixel. The regions of potential intersection are accumulated among the pixels to produce a cumulative array, and information in the cumulative array is processed for identifying spiculations in the digital mammogram.

Abstract: A method an apparatus for detecting lines in medical images is disclosed, wherein a direction image array and a line image array are formed by filtering a digital image with a single-peaked filter, convolving the resultant array with second order difference operators oriented along the horizontal, vertical, and diagonal axes, and computing the direction image arrays and line image arrays as direct scalar functions of the results of the second order difference operations. Advantageously, line detection based on the use of four line operator functions along the horizontal, vertical, and diagonal directions in accordance with the preferred embodiments actually results in fewer computations than line detection based on the use of three line operator functions. In particular, because of the special symmetries involved, 3×3 second order difference operators may be effectively used.

Abstract: A method and apparatus for the automated detection of masses in a digital mammogram, the method for use in a computer aided diagnosis system for assisting a radiologist in identifying and recognizing suspicious portions of the digital mammogram. A gradient image is created from the digital mammogram, and information in the gradient image is processed for identifying masses. In a preferred embodiment, a portion of a spiculation detection algorithm is applied to the gradient image for identifying masses. The spiculation detection algorithm comprises a line detection portion and a post-line detection portion, and it is the post-line detection portion which is applied to the gradient image for identifying masses. Advantageously, computer programs which have already been written for spiculation detection may, with minor modifications, be ported into mass detection programs.

Abstract: A method and apparatus for the fast detection of spiculated lesions in a digital mammogram, the method for use in a computer aided diagnosis system for assisting a radiologist in identifying and recognizing the spiculations among a multiplicity of lines corresponding to standard fibrous breast tissue. A line and direction image is created from a digital mammogram, and a region of potential intersection for substantially every pixel in the digital mammogram image is determined. The region of potential intersection for each pixel is a predetermined pattern, such as a high aspect ratio rectangle or trapezoid, positioned around the pixel and rotated in a direction corresponding to direction information for that pixel. The regions of potential intersection are accumulated among the pixels to produce a cumulative array, and information in the cumulative array is processed for identifying spiculations in the digital mammogram.

Abstract: A method and system for detecting suspicious portions of digital mammograms by using independently calculated mass and spiculation information is disclosed. The method is for use in a computer aided diagnosis system that is designed to bring suspicious or possibly cancerous lesions in fibrous breast tissue to the attention of a radiologist or other medical professional. In a preferred embodiment, spiculation information and mass information are independently calculated, with the computed spiculation information not being dependent on results of the mass information computation, thus leading to greater reliability. Systems according to a preferred embodiment also compute spiculation information either prior to, or concurrently with, the computation of mass information, thus allowing increased overall system speed.

Abstract: A system, method, and computer program product for computer-aided detection of suspicious lesions in digital mammograms is described, wherein single-view feature vectors from a first digital mammogram are processed in a classification algorithm along with information computed from a plurality of related digital mammograms to assign an overall probability of suspiciousness to potentially suspicious lesions in the first digital mammogram. In one preferred embodiment, a greater probability of suspiciousness is determined where there are similar corresponding lesions in the first digital mammogram and in an alternate digital mammogram view of the same breast. In another preferred embodiment, a lesser probability of suspiciousness is found where there are symmetric lesions or structures located in the first digital mammogram and a digital mammogram of the opposite breast.

Abstract: A method and apparatus for the automatic segmentation of the pectoral muscle boundary in a digital mammogram is disclosed. Parameters of the pectoral boundary line are determined by calculating gradient magnitudes of the digital mammogram in a region of interest, accumulating the gradient magnitudes into a parameter plane according to a Hough transform, and using information in the parameter plane for identifying the pectoral boundary. Prior to being accumulated in the parameter plane, the gradient magnitudes are weighted by a monotonic, multi-bit weighting function designed to exhibit larger variations near more densely populated levels of gradient magnitudes, and designed to exhibit smaller variations near less densely populated levels of gradient magnitudes, for allowing greater robustness against variations in gradient magnitudes among different digital mammograms.

Abstract: A method an system for using a local attention threshold to aid in the detection of clustered abnormalities in digitized medical images is disclosed. The local attention threshold is applied to locate spots within a predetermined distance from previously identified spots. More specifically, seed pixels are identified by applying a first seed threshold function to the output of a shift-invariant neural network and adaptive threshold. The seed pixels are then segmented into spots by applying a segmentation threshold function to each seed pixel. False-positive spots are removed using various techniques. Additional seed pixels are then identified by applying a local attention threshold to pixels within a predetermined distance to previously identified spots. The local attention threshold disclosed is less selective for pixels which are closer to the nearest spot than for pixels which are further from the nearest spot.

Abstract: A user interface for facilitating the input of films into a computer-aided diagnosis system is disclosed. The user interface includes a scanner which receives and scans the film-based images, and a film feeder which holds and transports them to the scanner. A touch sensitive display screen is provided to display status information to and receive instructions from an operator. The display screen displays miniature images of the films having color-coded borders so that the operator monitors the processing of the films. The interface also includes a bar code reader and allow the operator to conduct procedures for testing the scanner.

Abstract: A method and apparatus for the fast detection of spiculated lesions in a digital mammogram, the method for use in a computer aided diagnosis system for assisting a radiologist in identifying and recognizing the spiculations among a multiplicity of lines corresponding to standard fibrous breast tissue. A line and direction image is created from a digital mammogram, and a region of potential intersection for substantially every pixel in the digital mammogram image is determined. The region of potential intersection for each pixel is a predetermined pattern, such as a high aspect ratio rectangle or trapezoid, positioned around the pixel and rotated in a direction corresponding to direction information for that pixel. The regions of potential intersection are accumulated among the pixels to produce a cumulative array, and information in the cumulative array is processed for identifying spiculations in the digital mammogram.