Abstract: A system and method are provided for altering the appearance of an artificial object in a medical image. An artificial object is first identified in the medical image, such as identifying a breast implant in a mammography image. The prominence of the artificial object is then reduced, for example by suppressing the brightness or masking the artificial object out altogether. The resulting medical image with the altered artificial object is then displayed to a user so that the medical image can be more accurately analyzed without requiring the user to adjust the image on his or her own.

Abstract: A system and method is provided for enhancing a region of interest in a medical image to improve its visibility. A region of interest is first identified in the medical image, such as identifying a breast region in a mammography image. The identified region of interest is then enhanced using an image processing technique, for example by adjusting the intensity or contrast, or by performing edge enhancement. Other regions of the medical image outside the region of interest remain unaltered, or may be diminished, such that the clarity of the region of interest is improved in comparison with the other regions of the medical image. A user viewing the enhanced image is less distracted by the non-enhanced regions and is not required to adjust the image on his or her own. The user can more quickly and effectively review the medical image to identify abnormalities and diagnose disease.

Abstract: A system and method is provided for matching visual features in at least two related medical images. The differences in visual features between at least two related images are identified, such as separate mammography images of a left breast and a right breast of a patient. The visual features may include the image brightness, contrast, sharpness (or edge strength), alignment or dynamic range. The visual features of at least one of the images is then adjusted to match the visual features of the other image, or both images are adjusted to match a predefined value of visual features for a medical image. The adjusted images may then be displayed to a user, such as a radiologist or technician, who is able to more accurately identify physiological inconsistencies between the two images now that they have similar underlying visual features.

Abstract: A system and method are provided for the measurement and display of attributes of an object of interest in a medical image. An object of interest is identified, such as a lesion or cluster of lesions in a breast area on a mammography image. At least one attribute of the lesion is then automatically measured, such as the area of the lesion, the width and height of a cluster of lesions, the number of lesions in a cluster, or the distance from one or more lesions to an anatomical feature such as the nipple, skin line or chest wall. The measurements are then displayed to a user, for example by displaying the measurements on the mammography image. Additionally, anatomical zones, such as standard, quadrant and clock zones of the breast area may be determined and displayed on the mammography image or diagram to display the location of the lesion as it corresponds to the zones.

Abstract: A method reduces artifacts in an input image. A variance image is generated from the input image. The input image is partitioned into a plurality of blocks of pixels. A set of classifications is defined. The set of classifications includes smooth, texture, and edge. A particular classification is assigned to each block of pixels of the input image according to the variance image, to generate smooth blocks, texture blocks, and edge blocks. A fuzzy filter is applied to each pixel of only each edge block.

Abstract: A method filters pixels in an image, by first partitioning the image into blocks. Edge block are identified. A variance of an intensity for each pixel in each edge block is determined. Then, each pixel in each edge block is filtered with a filter that is dependant on the variance of the pixel.

Abstract: A method classifies pixels in an image by first partitioning the image into blocks. A variance of an intensity is determined for each pixel, and for each block the pixel with the maximum variance is identified. Then, the blocks are classified into classes according to the maximum variance.

Abstract: An apparatus, a computer-readable medium and a method of detecting cancer masses using mammography are described. From an input image, an iris contrast map and an iris ring filter response map of the input image are generated. Potential abnormal mass candidates are identified by locating those masses whose iris contrast value above a predetermined contrast threshold and whose iris ring filter response is above a predetermined response threshold. After the potential abnormal mass candidates are identified, candidates that are less likely to be abnormal can be eliminated.

Abstract: An invention provides a system and method for filtering pixels in an image using only fixed-point and summation operations. First, a filtering window is centered on an input pixel. Based on a difference between the intensity of the input pixel and its neighboring pixels, fuzzy filter weights are obtained. A sum of the fuzzy filter weights is used to determine a normalization factor. Then, the pixel intensities, fuzzy filter weights and the normalization factor are used to obtain an output pixel corresponding to the input pixel.

Abstract: A method classifies pixels in an image by first partitioning the image into blocks. A variance of an intensity is determined for each pixel, and for each block the pixel with the maximum variance is identified. Then, the blocks are classified into classes according to the maximum variance.

Abstract: A method reduces artifacts in an input image. A variance image is generated from the input image. The input image is partitioned into a plurality of blocks of pixels. A set of classifications is defined. The set of classifications includes smooth, texture, and edge. A particular classification is assigned to each block of pixels of the input image according to the variance image, to generate smooth blocks, texture blocks, and edge blocks. A fuzzy filter is applied to each pixel of only each edge block.

Abstract: A method filters pixels in an image, by first partitioning the image into blocks. Edge block are identified. A variance of an intensity for each pixel in each edge block is determined. Then, each pixel in each edge block is filtered with a filter that is dependant on the variance of the pixel.