Abstract: For each of a number of landmarks in an image an initial position of the landmark is defined. Next a neighborhood around the initial position comprising a number of candidate locations of the landmark, is sampled and a cost is associated with each of the candidate locations. A cost function expressing a weighted sum of overall gray level cost and overall shape cost for all candidate locations is optimized. A segmented anatomic entity is defined as a path through a selected combination of candidate locations for which combination the cost function is optimized. During optimization towards the optimal segmented surface/volume graph traversal methods are exploited.

Abstract: A basic component of Computer-Aided Detection systems for digital mammography comprises generating candidate mass locations suitable for further analysis. A component is described that relies on filtering either the background image or the complementary foreground mammographic detail by a purely signal processing method on the one hand or a processing method based on a physical model on the other hand. The different steps of the signal processing approach consist of band-pass filtering the image by one or more band pass filters, multidimensional clustering, iso-contouring of the distance to centroid of the one or more filtered values, and finally candidate generation and segmentation by contour processing. The physics-based approach also filters the image to retrieve a fat-corrected image to model the background of the breast, and the resulting image is subjected to a blob detection filter to model the intensity bumps on the foreground component of the breast that are associated with mass candidates.

Abstract: A Markov Random Field (MRF)-based technique is described for performing clustering of images characterized by poor or limited data. The proposed method is a statistical classification model that labels the image pixels based on the description of their statistical and contextual information. Apart from evaluating the pixel statistics that originate from the definition of the K-means clustering scheme, the model expands the analysis by the description of the spatial dependence between pixels and their labels (context), hence leading to the reduction of the inhomogeneity of the segmentation output with respect to the result of pure K-means clustering.

Abstract: Method to bring out a temporal difference between corresponding structures in a reference image R and a floating image F by convolving the reference image R and the floating image F with a window function Hw to generate Rw and Fw, applying a non-rigid transformation resulting in a transformation field g(rR) mapping every location rR to a corresponding location rF in the floating image F and generating a subtraction image by performing subtraction Rw(r)?Fw(g(r)) wherein r represents a voxel (x, y, z) in reference image R.

Abstract: A workflow method for temporal nodule review by registering a reference image R with a floating image F, convolving the reference image R and the floating image with the same window function Hw to generate Rw and Fw, generating a subtraction image by performing subtraction Rw?Fw (g(r)) wherein r represents a voxel (x, y, z) in reference image R, applying a pattern detector to said subtraction image to detect corresponding nodules in reference image R and floating image F and displaying corresponding nodules.

Abstract: An image point in a displayed reference image R is selected and a non-rigid transformation resulting in a transformation field g(rR) mapping every location rR to a corresponding location rF in a floating image F is applied, next a rigid body transformation is applied to floating image F such that rF coincides with the selected image point and the transformed floating image is displayed.

Abstract: A gray value model is generated encoding photometric knowledge at landmark positions. This step exploits intensity correlation in neighborhoods sampled around landmark positions. A geometric model is generated encoding geometric knowledge between landmarks. This step exploits spatial correlation between landmarks of segmented anatomic entities.

Abstract: A first and a second image are expressed in a common coordinate system by applying a geometric transformation to the second image so as to map a structure in the second image onto a corresponding structure in the first image in a common coordinate system. Starting from initial values, the parameters of the geometric transformation are updated taking into account the result of an evaluation of a cost function. Measurements are performed in the common coordinate system.

Abstract: A first and a second image are expressed in a common coordinate system by applying a geometric transformation to the second image so as to map a structure in the second image onto a corresponding structure in the first image in a common coordinate system. Starting from initial values, the parameters of the geometric transformation are updated taking into account the result of an evaluation of a cost function. Measurements are performed in the common coordinate system.

Abstract: A first and a second image are expressed in a common coordinate system by applying a geometric transformation to the second image so as to map a structure in the second image onto a corresponding structure in the first image in a common coordinate system. Starting from initial values, the parameters of the geometric transformation are updated taking into account the result of an evaluation of a cost function. Measurements are performed in the common coordinate system.

Abstract: A first and a second image are expressed in a common coordinate system by applying a geometric transformation to the second image so as to map a structure in the second image onto a corresponding structure in the first image in a common coordinate system. Starting from initial values, the parameters of the geometric transformation are updated taking into account the result of an evaluation of a cost function. Measurements are performed in the common coordinate system.

Abstract: A method of detecting the orientation of a radiographic image represented by a digital signal representation wherein mathematical moments of the digital signal representation are calculated relative to different reference entities and wherein a decision on the orientation of the radiographic image, for example the position of the thorax edge in a mammographic image, is obtained on the basis of an extreme value (minimum, maximum) of the calculated moments.

Abstract: For each of a number of landmarks in an image an initial position of the landmark is defined. Next a neighborhood around the initial position, comprising a number of candidate locations of the landmark is sampled and a cost is associated with each of the candidate locations. A cost function expressing a weighted sum of overall gray level cost and overall shape cost for all candidate locations is optimized. A segmented anatomic entity is defined as a path through a selected combination of candidate locations for which combination the cost function is optimized.

Abstract: A first and a second image are expressed in a common coordinate system by applying a geometric transformation to the second image so as to map a structure in the second image onto a corresponding structure in the first image in a common coordinate system. Starting from initial values, the parameters of the geometric transformation are updated taking into account the result of an evaluation of a cost function. Measurements are performed in the common coordinate system.

Abstract: A method and system for acquiring information on lesions in dynamic 3D medical images of a body region and/or organ, having the steps: applying a registration technique to align a plurality of volumetric image data of the body region and/or organ, yielding multi-phase registered volumetric image data of the body region and/or organ; applying a hierarchical segmentation on the multi-phase registered volumetric image data of the body region and/or organ, the segmentation yielding a plurality of clusters of n-dimensional voxel vectors of the multi-phase aligned volumetric image; determining from the plurality of clusters a cluster or set of clusters delineating the body and/or organ; identifying the connected region(s) of voxel vectors belonging to the body region and/or organ; refining/filling the connected region(s) corresponding to the body region and/or organ; reapplying the segmentation step to the refined/filled connected region(s) corresponding to the body region and/or organ, to obtain a more accurate se

Abstract: For each of a number of landmarks in an image an initial position of the landmark is defined. Next a neighborhood around the initial position, comprising a number of candidate locations of the landmark is sampled and a cost is associated with each of the candidate locations. A cost function expressing a weighted sum of overall gray level cost and overall shape cost for all candidate locations is optimized. A segmented anatomic entity is defined as a path through a selected combination of candidate locations for which combination the cost function is optimized.

Abstract: For each of a number of landmarks in an image an initial position of the landmark is defined. Next a neighborhood around the initial position, comprising a number of candidate locations of the landmark is sampled and a cost is associated with each of the candidate locations. A cost function expressing a weighted sum of overall gray level cost and overall shape cost for all candidate locations is optimized. A segmented anatomic entity is defined as a path through a selected combination of candidate locations for which combination the cost function is optimized.

Abstract: A method of detecting the orientation of a radiographic image represented by a digital signal representation wherein mathematical moments of the digital signal representation are calculated relative to different reference entities and wherein a decision on the orientation of the radiographic image, for example the position of the thorax edge in a mammographic image, is obtained on the basis of an extreme value (minimum, maximum) of the calculated moments.

Abstract: A method of detecting the orientation of a radiographic image represented by a digital signal representation wherein mathematical moments of the digital signal representation are calculated relative to different reference entities and wherein a decision on the orientation of the radiographic image, for example the position of the thorax edge in a mammographic image, is obtained on the basis of an extreme value (minimum, maximum) of the calculated moments.

Abstract: For each of a number of landmarks in an image an initial position of the landmark is defined. Next a neighborhood around the initial position comprising a number of candidate locations of the landmark, is sampled and a cost is associated with each of the candidate locations. A cost function expressing a weighted sum of overall gray level cost and overall shape cost for all candidate locations is optimized. A segmented anatomic entity is defined as a path through a selected combination of candidate locations for which combination the cost function is optimized. During optimization towards the optimal segmented surface/volume graph traversal methods are exploited.