Abstract: A database system, method and computer program product conduct efficient searches of a database to reliably identify a subset of relevant reports. The database system includes report encoding circuitry to encode a first report into a feature vector based upon the content of the first report. The database system also includes report identification circuitry to identify a closest prototype of the feature vector representative of the first report from among a plurality of prototypes. Each prototype is representative of a cluster of feature vectors of respective reports stored in a database. From among the cluster of feature vectors of respective reports of the closest prototype, the report identification circuitry identifies one or more of the feature vectors that are closest to the feature vector representative of the first report and provides an indication of the respective report(s) represented by the one or more feature vectors that have been identified.

Abstract: A method, image processing system and computer program product are provided for detecting an anatomical marker within an image study. In the context of a method, data elements associated with image slices of the image study of a patient are accessed. The data elements are representative of one or more characteristics of the image slices, but do not include picture elements that comprise the image slices. The method also includes reviewing the data elements associated with at least some image slices of the image study of the patient. The method further includes detecting the anatomical marker within the image study based upon a review of the data elements and identifying a subset of the image slices of the image study based upon the anatomical marker or a distance from the anatomical marker.

Abstract: A method, apparatus and computer program product are hereby provided to detect anatomical elements in a medical image. In this regard, the method, apparatus, and computer program product may receive a test image and generate a classified image by applying an image classifier to the test image. The image classifier may include at least one decision tree for evaluating at least one pixel value of the test image and the classified image may include a plurality of pixel values. Each pixel value may be associated with a probability that an anatomical element is located at the pixel location. The method, apparatus, and computer program product may also evaluate the classified image using an anatomical model to detect at least one anatomical element within the classified image.

Abstract: A method, a computing device and a computer program product are provided in order to automatically apply window leveling to an image, such as a medical image. In the context of a method, a gaze location within an image is determined based upon a determination that a user is staring at the gaze location. The method also determines a region of interest within the image based upon the gaze location and determines pixel values for pixels within the region of interest. The method also establishes window level values based upon the pixel values for pixels within the region of interest and applies window leveling based upon the window level values established based upon the pixel values for pixels within the region of interest.

Abstract: A method, a computing device and a computer program product are provided to identify an instance in which a pixel visibility loss condition exists following window leveling. In the context of a method, window and level values and corresponding minimum and maximum values for a window are identified. The method then determines whether the pixel visibility loss condition exists based upon one or more of the window and level values, the minimum value or the maximum value. In this regard, the method determines whether a pixel visibility loss condition exists by determining whether there is a gray scale loss, a gray scale compression or a lack of perceptible distinctiveness between pixels within the window. In an instance in which the pixel visibility loss condition exists, the method causes an indication to be provided to a user.

Abstract: A method, image processing system and computer program product are provided for detecting an anatomical marker within an image study. In the context of a method, data elements associated with image slices of the image study of a patient are accessed. The data elements are representative of one or more characteristics of the image slices, but do not include picture elements that comprise the image slices. The method also includes reviewing the data elements associated with at least some image slices of the image study of the patient. The method further includes detecting the anatomical marker within the image study based upon a review of the data elements and identifying a subset of the image slices of the image study based upon the anatomical marker or a distance from the anatomical marker.

Abstract: A method, apparatus and computer program product are provided to enhance medical images by utilizing an enhancement function in order to permit a user to more efficiently and effectively read the medical images and provide diagnostic or other information based upon the enhanced images. In the context of a method, a medical image is received that has a plurality of pixels with respective pixel values. The method also enhances the medical image utilizing an enhancement function. The enhancement function varies depending upon the pixel value of a respective pixel. For example, the enhancement function of one embodiment may be a piecewise function. As such, the method enhances the medical image by modifying a representation of the medical image in accordance with the enhancement function.

Abstract: An apparatus, method and computer-readable storage medium are provided for processing images, such as medical images, in accordance with an imaging calibration function during execution of an image viewing application, and also for processing other types of images with a different calibration function during execution of a different application. In regards to an apparatus, the apparatus is caused to identify, in conjunction with an image viewing application, an imaging calibration function. The apparatus is also caused to transform a digital image during execution of the image viewing application in accordance with the imaging calibration function and to output the digital image, following transformation, in accordance with the image viewing application.

Abstract: A PACS viewer and an associated method and computer program product are provided for identifying patient orientation. With respect to a PACS viewer, the PACS viewer includes processing circuitry configured to register an image onto a template. The processing circuitry is also configured to determine a representative seed point within each of the left and right lungs as represented by the image. The processing circuitry is further configured to apply an active contour model to each of the left and right lungs to generate a binary image of the left and right lungs. In this regard, the processing circuitry is configured to apply the active contour model by initializing the active contour model with a mask constructed using the representative seed points. Further, the processing circuitry is configured to detect at least one feature from the binary image to permit identification of the left and right lungs.

Abstract: A PACS viewer and an associated method and computer program product are provided for identifying patient orientation. With respect to a PACS viewer, the PACS viewer includes processing circuitry configured to register an image onto a template. The processing circuitry is also configured to determine a representative seed point within each of the left and right lungs as represented by the image. The processing circuitry is further configured to apply an active contour model to each of the left and right lungs to generate a binary image of the left and right lungs. In this regard, the processing circuitry is configured to apply the active contour model by initializing the active contour model with a mask constructed using the representative seed points. Further, the processing circuitry is configured to detect at least one feature from the binary image to permit identification of the left and right lungs.

Abstract: A method, a computing device and a computer program product are provided to identify an instance in which a pixel visibility loss condition exists following window leveling. In the context of a method, window and level values and corresponding minimum and maximum values for a window are identified. The method then determines whether the pixel visibility loss condition exists based upon one or more of the window and level values, the minimum value or the maximum value. In this regard, the method determines whether a pixel visibility loss condition exists by determining whether there is a gray scale loss, a gray scale compression or a lack of perceptible distinctiveness between pixels within the window. In an instance in which the pixel visibility loss condition exists, the method causes an indication to be provided to a user.

Abstract: A method, a computing device and a computer program product are provided in order to automatically apply window leveling to an image, such as a medical image. In the context of a method, a gaze location within an image is determined based upon a determination that a user is staring at the gaze location. The method also determines a region of interest within the image based upon the gaze location and determines pixel values for pixels within the region of interest. The method also establishes window level values based upon the pixel values for pixels within the region of interest and applies window leveling based upon the window level values established based upon the pixel values for pixels within the region of interest.

Abstract: An apparatus, method and computer-readable storage medium are provided for processing images, such as medical images, in accordance with an imaging calibration function during execution of an image viewing application, and also for processing other types of images with a different calibration function during execution of a different application. In regards to an apparatus, the apparatus is caused to identify, in conjunction with an image viewing application, an imaging calibration function. The apparatus is also caused to transform a digital image during execution of the image viewing application in accordance with the imaging calibration function and to output the digital image, following transformation, in accordance with the image viewing application.

Abstract: Methods, apparatuses, and computer program products are provided for identifying a region of interest within a mammogram image. A method may include applying a clustering algorithm to a histogram of the mammogram image to identify a predefined number of threshold values. The method may further include determining a predefined number of seed values based at least in part on the identified threshold values. The method may additionally include generating a kernel image for each of the seed values. The method may also include using the generated kernel images to identify a region of interest including a breast within the mammogram image. Corresponding apparatuses and computer program products are also provided.

Abstract: A method, apparatus and computer program product are hereby provided to detect anatomical elements in a medical image. In this regard, the method, apparatus, and computer program product may receive a test image and generate a classified image by applying an image classifier to the test image. The image classifier may include at least one decision tree for evaluating at least one pixel value of the test image and the classified image may include a plurality of pixel values. Each pixel value may be associated with a probability that an anatomical element is located at the pixel location. The method, apparatus, and computer program product may also evaluate the classified image using an anatomical model to detect at least one anatomical element within the classified image.

Abstract: An apparatus is provided that includes a processor and memory storing executable instructions that in response to execution by the processor cause the apparatus to at least perform a number of operations. The apparatus is caused to receive a digital image including pixels each of which has a pixel value that has been calibrated according to a first calibration function for calibrating an image for display by a first monitor. The apparatus is caused to transform the pixel value of each of at least some of the pixels to a corresponding transformed pixel value calibrated according to a second calibration function for calibrating an image for display by a second monitor. The apparatus is also caused to cause output of the digital image including the plurality of pixels each of at least some of which has a transformed pixel value, the respective digital image being displayable by the second monitor.

Abstract: Methods, apparatuses, and computer program products are provided for identifying a region of interest within a mammogram image. A method may include applying a clustering algorithm to a histogram of the mammogram image to identify a predefined number of threshold values. The method may further include determining a predefined number of seed values based at least in part on the identified threshold values. The method may additionally include generating a kernel image for each of the seed values. The method may also include using the generated kernel images to identify a region of interest including a breast within the mammogram image. Corresponding apparatuses and computer program products are also provided.

Abstract: Methods, apparatuses, and computer program products are provided for determining a modulation transfer function of an imaging system. A method may include accessing an image of a phantom having a substantially circular shaped feature captured by the imaging system. The method may further include detecting the circular shaped feature within the image. The method may additionally include defining at least one line extending from a point within the detected circular shaped feature to a point outside of the circular shaped feature. The method may also include determining an edge spread function based at least in part on the defined at least one line. The method may further include determining the modulation transfer function of the imaging system based at least in part on the determined edge spread function. Corresponding apparatuses and computer program products are also provided.

Abstract: Methods, apparatuses, and computer program products are provided for identifying a region of interest within a mammogram image. A method may include applying a clustering algorithm to a histogram of the mammogram image to identify a predefined number of threshold values. The method may further include determining a predefined number of seed values based at least in part on the identified threshold values. The method may additionally include generating a kernel image for each of the seed values. The method may also include using the generated kernel images to identify a region of interest including a breast within the mammogram image. Corresponding apparatuses and computer program products are also provided.

Abstract: A method, apparatus and computer program product are provided to enhance medical images by utilizing an enhancement function in order to permit a user to more efficiently and effectively read the medical images and provide diagnostic or other information based upon the enhanced images. In the context of a method, a medical image is received that has a plurality of pixels with respective pixel values. The method also enhances the medical image utilizing an enhancement function. The enhancement function varies depending upon the pixel value of a respective pixel. For example, the enhancement function of one embodiment may be a piecewise function. As such, the method enhances the medical image by modifying a representation of the medical image in accordance with the enhancement function.