Abstract: Methods and apparatuses perform thickness compensation in anatomical images. The method according to one embodiment accesses digital image data representing an image including a breast; estimates thickness of the breast at multiple locations inside the breast using an image data characteristic and a reference tissue in the breast; compensates thickness of the breast using a thickness model; and refines compensation of breast thickness from the compensating step.

Abstract: Methods and apparatuses disclosed herein process medical images, for comparison and analysis of the images. The method according to one embodiment accesses digital image data representing a first medical image and a second medical image; registers the second image to the first image using a specific region preserving registration or specific regions preserving registration, to obtain a registered second image; and compares the first image and the registered second image.

Abstract: A method and an apparatus process images. The method according to one embodiment accesses digital image data representing an image including an object; clusters pixels of the image to obtain clusters; generates a graph with pixels of the image and cluster membership information of pixels in the clusters; and segments the graph using a max-flow segmentation to obtain pixels of the image associated with the object.

Abstract: Methods and apparatuses process images. The method according to one embodiment accesses digital image data representing an image including an object; generates a connected graph associated with the image, the generating step including representing pixels of the image in a higher than two dimensional space to obtain pixel representations, generating a pixel representation graph using the pixel representations, and assigning weights to edges between the pixel representations in the pixel representation graph, based on a gradient characteristic between the pixel representations, to obtain a connected graph; and segments the connected graph using an energy minimizing function, to obtain pixels of the image associated with the object.

Abstract: Methods and apparatuses disclosed herein process medical images, for comparison and analysis of the images. The method according to one embodiment accesses digital image data representing a first medical image and a second medical image; registers the second image to the first image using a specific region preserving registration or specific regions preserving registration, to obtain a registered second image; and compares the first image and the registered second image.

Abstract: A method determines alignment of images in high dimensional feature space. The method comprises registering a source image of a reference modality to a target image of a second modality with an algorithm based upon a measure of information affinity present in both of the source and target image to create a registered image. Next, a plurality of feature vector are extracted from the registered image for each of the source and target images and attributes of the joint distribution of feature vector are captured using an entropic graph spanning the features. Edge lengths are between proximal feature vectors are extracted from the entropic graph and a similarity measure of one of an ?-divergence estimate or an ?-affinity estimate is constructed based upon these edge lengths to quantify whether the source and target image are sufficiently registered.

Abstract: Methods and apparatuses align breast images. The method according to one embodiment accesses digital image data representing a first breast image including a left breast, and a second breast image including a right breast; removes from the first and second breast images artifacts not related to the left and right breasts; and aligns the left and right breasts using a similarity measure between the first and second breast images, the similarity measure depending on a relative position of the first and second breast images.

Abstract: Methods and apparatuses perform thickness compensation in anatomical images. The method according to one embodiment accesses digital image data representing an image including a breast; estimates thickness of the breast at multiple locations inside the breast using an image data characteristic and a reference tissue in the breast; compensates thickness of the breast using a thickness model; and refines compensation of breast thickness from the compensating step.

Abstract: A method and an apparatus process images. The method according to one embodiment accesses digital image data representing an image including an object; clusters pixels of the image to obtain clusters; generates a graph with pixels of the image and cluster membership information of pixels in the clusters; and segments the graph using a max-flow segmentation to obtain pixels of the image associated with the object.

Abstract: Methods and apparatuses process images. The method according to one embodiment accesses digital image data representing an image including an object; generates a connected graph associated with the image, the generating step including representing pixels of the image in a higher than two dimensional space to obtain pixel representations, generating a pixel representation graph using the pixel representations, and assigning weights to edges between the pixel representations in the pixel representation graph, based on a gradient characteristic between the pixel representations, to obtain a connected graph; and segments the connected graph using an energy minimizing function, to obtain pixels of the image associated with the object.

Abstract: A method determines alignment of images in high dimensional feature space. The method comprises registering a source image of a reference modality to a target image of a second modality with an algorithm based upon a measure of information affinity present in both of the source and target image to create a registered image. Next, a plurality of feature vectors are extracted from the registered image for each of the source and target images and attributes of the joint distribution of feature vectors are captured using an entropic graph spanning the features. Edge lengths are between proximal feature vectors are extracted from the entropic graph and a similarity measure of one of an ?-divergence estimate or an ?-affinity estimate is constructed based upon these edge lengths to quantify whether the source and target image are sufficiently registered.