A method is described for estimating the bone quality of a vertebrate on the basis of two-dimensional image data comprising information relating to the trabecular structure of at least part of a bone of the vertebrate, the image data being data obtained by exposing at least the part of the bone to electromagnetic radiation, the method comprising subjecting the image data to a statistical analysis comprising deriving features from a parametric estimate of a power spectrum and/or deriving features from a transform similar to a distance transform--but in a number of directions.
In a method for the post-processing of a tomogram, and a computed tomography apparatus operating according to this method, a universal computer is employed for post-processing of a reconstructed tomogram of a slice of an examination subject, the tomogram representing the entire field of measurement of the computed tomography apparatus or a segment of the field of measurement. The post-processing employs an adaptive ring suppression filter, in which the pixel values of a region comprising the entire tomogram or a part of the tomogram are subjected to, among other things, one or more median filterings, and an averaging, with the median filterings and the averaging take place along a number of directions of execution, and the calculation of the pixel values of the region takes place so that the pixel values of successive pixels in the each processing direction are calculated successively.
Image acquisition and analysis systems and methods are provided. A ray-based approach may be used to process images for cell-based assays. Such cell-based assays may be used to evaluate drugs or other compounds or to perform other biological studies. A scanning laser microscope or other equipment may be used to gather image data from fluorescently-marked cells or other suitable specimens. The rays are radially-oriented with respect to the cell nuclei. Seed points within the nuclei may be identified. The rays may extend outward from the seed points or other suitable ray origins until the rays are terminated according to ray termination criteria. The intensity of the image data that is associated with each of the rays may be analyzed to generate various parameters. For example, a peak intensity of the image data along each ray may be identified. Statistical calculations may be performed.
A computer-aided diagnosis (CAD) method for the automated detection of lung nodules in a digital chest image, a computer programmed to implement the method, and a storage medium which stores a program for implementing the method, wherein nodule candidates are first automatically selected by thresholding the difference image and then classified in six groups. A large number of false positives are eliminated by adaptive rule-based tests applied to the original chest image and in the difference image and an artificial neural network (ANN) applied to remaining candidate nodule locations in the original chest image. Using two hundred PA chest radiographs, 100 normal and 100 abnormal, as the database, the presence of nodules in the 100 abnormal cases was confirmed by two experienced radiologists on the basis of CT scans or radiographic follow-up. The CAD method achieves, on average, the sensitivity of 70% at 1.7 false positives per chest image.
Several methods for retrospective correction of intensity inhomogeneities in digital diagnostic radiation images are presented. The methods are based on the correction of a digital image representation by means of a bias field. The bias field is deduced from the digital image representation of the diagnostic radiation image.
Several methods for retrospective correction of intensity inhomogeneites in digital diagnostic radiation images are presented. The methods are based on the correction of a digital image representation by means of a bias field. The bias field is deduced from the digital image representation of the diagnostic radiation image.
