Image analysis – Applications – Biomedical applications
Reexamination Certificate
1998-11-03
2002-07-23
Boudreau, Leo (Department: 2621)
Image analysis
Applications
Biomedical applications
C128S922000, C356S039000
Reexamination Certificate
active
06424730
ABSTRACT:
FIELD OF THE INVENTION
This invention relates in general to a method for the enhancement of medical images for hardcopy prints. More particularly, it relates to such a method which compensates for the losses of image high frequency contents due to the limited capability of the output device and which mimics the image tone scale on the hardcopy prints of the trans-illuminated film on a standard diagnostic lightbox.
BACKGROUND OF THE INVENTION
As picture archiving and communication systems (PACs) have been widely installed and tested in the clinical environment, a virtual radiology department with multi-facilities has been gradually established in which medical images can be accessed and viewed using softcopy displays anywhere across the entire computer network. In such a situation, hardcopy prints of medical images seem obsolete and unnecessary. However, this is not true and hardcopies will always be needed for various purposes. For example, for referral medical report an attached image can greatly facilitate the descriptions.
Photographic inkjet printers and direct thermal printers are among the best choices for non-diagnostic, referral printing due to their wide availability, low cost and portability. However, when using such a printer to directly print medical images for the referral purpose, there are several major challenges: (1) reduced image local contrast and blurred details due to the limited spatial resolution of the printer, and (2) image tone scale which is different from trans-illuminated film because of either the limited density dynamic range of the printer or the reduced viewing luminance.
The spatial frequency bandwidth of both inkjet and direct thermal printers is limited due to the halftone algorithms used in the printer technology. As a result, the printed image look quite blurred and local contrast in the image is significantly reduced. Consequently, important diagnostic details, which are often associated with the high frequency contents of the image, are undesirably suppressed. To compensate for the limited spatial resolution of the printer, it is essential to enhance the image details prior to printing.
When a medical image is printed on a reflection medium such as paper, or a trans-illuminated medium such as transparency, its tone scale is usually different from its film image. The optical density which can be produced on the medium and the image viewing luminance are two dominant factors in causing the tone scale difference. For the same image, when viewing in an indoor ambient light condition, a reflection print always looks darker in the mid-tone region than the film displayed on a lightbox, even if they are printed using the same optical density. The luminance of a lightbox used in the standard diagnostic environment is approximately 2,500 cd/m
2
, and the typical minimum and maximum optical densities on a film are 0.21 and 3.0, respectively. Therefore, the maximum transmitance luminance produced by the film/lightbox is more than 1500 cd/m
2
and an luminance dynamic range of 620:1 can be achieved. Under such a high luminance level and wide dynamic range, a typical human observer can tell around 800 Just Noticeable Differences (JNDs) of luminance changes in an image, based on Barten's model of the human visual system (HVS) using a standard target (H. Blume, “The act
ema proposal for a grey-scale display function standard,” SPIE Medical Imaging, Vol. 2707, pp. 344-360, 1996).
On the other hand, the luminance of an indoor environment with diffusive light condition varies dramatically, from about fifty to several hundred cd/m
2
. The typical minimum and maximum densities on a photographic reflection print are around 0.1 and 2.2, respectively, which generate a dynamic range of 120:1 and a maximum reflectance luminance of 120 cd/m
2
if a luminance of 150 cd/m
2
is assumed for a surface with 100% reflectance. Again based on Barten's model there are only about 400 JNDs that can be perceived on the reflection print. When using a photographic printer to print an image on a transparency, the produced density range is usually lower than a film. If such an image is displayed on the lightbox, a less number of JNDs can be perceived on the transparency than on the film, which make the image look flat.
Other factors associated with the photographic printers, such as quantization of the input data, further reduce the JNDs that can be perceived on a hardcopy print. Photographic printers ordinarily take only 8-bit data input and therefore can print 256 greylevels at most. On the other hand, film printers often use 12-bit data input and are able to generate sufficient greylevels for diagnostic purposes.
It is preferable to display a medical image on a hardcopy print in a consistent way and with the similar perceptual image quality of film. In the clinical field, many x-ray images are evaluated on films for primary diagnostics even if digital x-ray images exist and softcopy displays are available. Also, images from other modalities, such as Computed Tomography (CT) or Magnetic Resonance Imaging (MRI), are often printed and viewed on films. A number of reasons can be given for this including cost, legal liability, as well as the unquestionable fact that most radiologists acquired their reading skills by viewing hardcopy films. Therefore, it is highly recommended to use film images as the gold standard for visualization and generate hardcopy prints that have similar tone scale to films.
Important diagnostic details are often associated with certain image features such as edges or textures. Enhancement of these image features usually involves in increasing local contrast and sharpening edges. For this purpose, histogram equalization and unsharp masking are two major classes of techniques.
A histogram equalization technique based on the whole image content is suitable to enhance the global image contrast instead of local contrast, and it sometimes attenuates the contrast of the image regions which have scarce population. On the other hand, a histogram equalization technique based on the local image content is designed to enhance the local contrast. However, it is computationally expensive (R. C. Gonzalez and R. E. Woods,
Digital Image Processing
, pp. 173-189, Addison-Wesley Publishing Company, 1993). In addition, histogram equalization techniques do not sharpen edges in order to address the problem caused by the limited spatial resolution of a printer.
An unsharp masking technique, broadly speaking, consists of decomposing an image into a low frequency component and a high frequency component, manipulating either component individually then combining them together. Since edges and texture in an image are usually the high frequency content, edge sharpening and local contrast enhancement can be achieved by boosting the high frequency component.
Various approaches have been developed for unsharp masking technique to decide and control the low and high frequency components of an image. The high frequency component can be determined either using a linear or a nonlinear high pass filter. Certain quadratic operators are commonly used as nonlinear filters to conduct the high pass operation (G. Ramponi, N. Strobel, S. K. Mitra, and T-H. Yu, “Nonlinear unsharp masking methods for image contrast enhancement,”
Journal of Electronic Imaging
, Vol. 5, No. 3, pp. 353-366, 1996). On the other hand, since the high frequency component of an image can be computed as the difference between the original image and a low pass filtered version of that image, a median filter, which is nonlinear, can be used for low pass filtering to obtain the high frequency component (R. C. Gonzalez and R. E. Woods,
Digital Image Processing
, pp. 191-195, Addison-Wesley Publishing Company, 1993). However, the computation is usually expensive for a nonlinear filter with a large kernel. A convolution operator with positive coefficients around its center and negative coefficients in the outer periphery, for example an Laplacian filter, constitutes a linear high pass filter. Due to the fact that a li
VanMetter Richard L.
Wang Xiao-hui
Boudreau Leo
Choobin M B
Eastman Kodak Company
Noval William F.
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