Image analysis – Applications – Biomedical applications
Reexamination Certificate
1999-11-29
2004-04-06
Boudreau, Leo (Department: 2621)
Image analysis
Applications
Biomedical applications
C382S128000
Reexamination Certificate
active
06718056
ABSTRACT:
CROSS-REFERENCE TO RELATED APPLICATIONS
This application claims the benefit of a priority under 35 USC 119(
a
)-(
d
) to French Patent Application No. 98 14996 filed Nov. 27, 1998, the entire contents of which are hereby incorporated by reference.
BACKGROUND OF THE INVENTION
This invention relates to a method for automatic determination of the brightness and contrast of a digitized radiographic image of an object.
It finds a particularly interesting application in the medical field in which fine analyses of radiographic images are made, notably. mammography.
In the medical field, diagnoses generally are based on the study of a radiographic image. The progress of digital systems now makes it possible to vary the characteristics of the image in order to best effect a diagnosis. Thus, it is evident that the quality of the image is an essential point.
As is well known, image acquisition introduces numerous parameters related to the acquisition chain: These include the parameters of acquisition (target material of the anode of the X-ray tube [track], material and thickness of the filter at the output of the X-ray tube [filter], voltage applied between cathode and anode of the X-ray tube [kV], heating current of the filament of the cathode of the X-ray tube [mA], exposure time, . . . ), the parameters of the positioner (incidence of the view, enlargement factor, type of compression pad used, thickness of the object, force of compression, . . . ) and the parameters of the digital detector (relation of gray levels to flux of X-rays captured . . . ). In addition to this, the parameters of the object, in particular its composition, are introduced. This set of parameters makes it possible to obtain an image that is in fact composed of pixels of shades of gray. Quantification of the image is obtained on a given number of gray levels. When the image is displayed on a screen or imprinted on a film using a given range of gray levels, the contrast perceived may be considerably different among images of the same object acquired with different sets of parameters of acquisition or among images of different objects obtained with the same set of parameters. In addition, selection of the range of gray level to be considered for the screen is of great importance in order to give an acceptable perception of contrast in the image. Thus, once the image has been obtained, the user interactively modifies the brightness (WL: window level) and the contrast (WW: window width) of this image in order to adjust the range of gray levels in it so as to obtain a correct perception of contrast.
Generally, for more than 16000 different gray levels, it is a difficult and lengthy task to select the correct brightness and contrast manually.
SUMMARY OF THE INVENTION
An embodiment of the invention permits automatically determining the value of the brightness (WL) and to derive the contrast (WW) from it.
An embodiment of the invention therefore proposes a method of digital radiographic image acquisition of an object with automatic adjustment of the parameters of visualization (brightness, contrast). The method is adaptable, since the wishes of the user may be taken into account.
This method comprises the obtaining of two gray levels, min gray and max gray, revealing a zone of gray levels in which the brightness (WL) is capable of being determined, a set of rules for calculation of the value WL, and from this, lastly, the derivation of a range of gray levels for the contrast WW.
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patent: 5544219 (1996-08-01), Muller et al.
patent: 5615279 (1997-03-01), Yoshioka et al.
patent: 5696805 (1997-12-01), Gaborski et al.
patent: 6215900 (2001-04-01), Schwenker et al.
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patent: 3214725 (1983-10-01), None
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patent: 9837738 (1998-08-01), None
Lienard, J., LSD/AAP Reference 98030 Technical Note, GE Medical Systems, Aug. 26, 1998, “Mammography Histogram Understanding”.
Bothorel Sylvie
Lienard Jean
Muller Serge
Nicolas François
Rick Andreas
Boudreau Leo
Cantor & Colburn LLP
Chaskin Jay L.
GE Medical Systems SA
Lu Tom Y.
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