Agfa-Gevaert

Agfa Healthcare Nv

Agfa Healthcare

Agfa Gevaert Nv

Katholieke Universiteit Leuven, Ku Leuven R&d

Piet Dewaele

Dieter Seghers

Gert Behiels

Paul Suetens

Dirk Loeckx

Pieter Paul Vuylsteke

Piet Vuylsteke

Peter M Willems

Tom Guido Buytaert

Samar Mohamed

Herman Pauwels

Guy Marchal

Bart Vaessen

Yener Onel

Uwe Ewert

Edgard Nyssen

Rudi Deklerck

Marek Suliga

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.

Method of segmenting anatomic entities in 3D digital medical images

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.

Method for mass candidate detection and segmentation in digital mammograms

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.

Method for segmenting digital medical image

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 Hto generate Rand F, generating a subtraction image by performing subtraction R&#x2212;F(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.

Visual enhancement of interval changes using temporal subtraction and pattern detector

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 Hto generate Rand F, applying a non-rigid transformation resulting in a transformation field g(r) mapping every location rto a corresponding location rin the floating image F and generating a subtraction image by performing subtraction R(r)&#x2212;F(g(r)) wherein r represents a voxel (x, y, z) in reference image R.

Visual enhancement of interval changes using temporal subtraction, convolving, and non-rigid transformation field mapping

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

Visual enhancement of interval changes using rigid and non-rigid transformations

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.

Method of constructing gray value or geometric models of anatomic entity in medical image

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.

Method of performing measurements on digital images

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.

Method of detecting the orientation of an object in an image

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.

Method of segmenting anatomic entities in digital medical images

The orientation of an image such as a radiographic image is deduced from the digital representation of the image. In one embodiment the curvature is calculated and a decision on the orientation of the image is obtained on the basis of the value of the calculated curvature.

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Berchem, Belgium

Piet Dewaele