Image analysis – Image transformation or preprocessing – Changing the image coordinates
Reexamination Certificate
1998-09-11
2001-05-22
Boudreau, Leo (Department: 2721)
Image analysis
Image transformation or preprocessing
Changing the image coordinates
C382S254000, C382S279000
Reexamination Certificate
active
06236766
ABSTRACT:
CROSS-REFERENCE TO RELATED APPLICATIONS
Not applicable.
STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT
Not applicable.
BACKGROUND OF THE INVENTION
The present invention relates to imaging methods and apparatus and more specifically to a method and apparatus which facilitates magnification or zooming of a portion of an image on a display.
Digital imaging is extremely important in many different applications. For example, digital imaging has proven invaluable in the medical imaging art where vast amounts of data are collected and used to generate images for observation on a video monitor or display. Although not so limited, in the interest of simplifying this explanation the present invention will be described in the context of medical imaging. A typical display includes a two-dimensional raster of pixels. For the purpose of this explanation, although raster pixels may be arranged in any of several different configurations, it will be assumed that pixels are arranged in distinct rows and columns.
To generate images on the display a processor collects all image data and generates intensity signals or pixel values for each display pixel. The pixel values are provided to a video driver which excites each pixel separately in accordance with an associated pixel value. From a distance the contrast between display pixel intensities is seen as an image.
Magnification of digital images in real time is needed in many different applications. For example, it may be advantageous to magnify a section of a medical image which includes a tumor. To this end, software has been developed which allows a user to select an image section for magnification and then magnifies the selected section.
One problem with image magnification has been selecting pixel intensities in a magnified image which reflect the initial image and result in a clean magnified image. For example, assume an intensity range between 0 and 100 where first and second adjacent pixel values correspond to intensities 55 and 92. Also assume that upon magnification, the area corresponding to the first and second pixels increases and covers 32 pixels (e.g. 16 pixels each). In this case, if the 16 magnified pixels corresponding to the first pixel and the 16 magnified pixels corresponding to the second pixel are provided with intensities 55 and 92, respectively, and all other magnified pixels are similarly magnified, the result is a highly granulated image which, in many cases, is not useful for the intended purpose of closer examination.
Instead of exciting pixels in the magnified image as indicated above (i.e. with either intensities 55 or 92), other solutions have been adopted by the industry with varying success and at varying costs. The most common methods for calculating new pixel intensities are nearest neighbor, bilinear and bicubic interpolation methods.
According to the nearest neighbor method, when points corresponding to first and second adjacent pixels on an initial image are separated by magnification so that the initial image points correspond to third and fourth pixels which are separated by a plurality of other pixels, the intensity of each of the other pixels (e.g. pixels between the third and fourth) are set equal to the intensity of the closest of the third or fourth pixel. This solution has the advantage of being computationally simple and therefore can be implemented easily using existing imaging hardware. Unfortunately, nearest neighbor methods only increase magnified image quality slightly and therefore are unacceptable for many applications.
Bilinear methods generally linearly fill in pixel intensities. For instance, in the example above where points corresponding to first and second adjacent pixels on an initial image are separated by magnification so that the initial image points correspond to third and fourth pixels which are separated by three other pixels and the first and second pixel intensities were 55 and 92, respectively, the other pixel intensities are linearly determined and are approximately 64, 73 and 83. High speed bilinear image magnification is now commonly available in accelerated graphics hardware. Unfortunately, while this solution generates a better image than the nearest neighbor methods, this solution requires much more processor time to perform necessary computations and still does not provide an extremely accurate magnification.
Bicubic interpolation methods generally take into account the intensities of more than just first and second adjacent pixels when determining the intensities of pixels in a magnified image which are between image points which correspond to the initial first and second pixels. In effect, these interpolation methods mathematically identify pixel intensities on one or more curves wherein the curves correspond to proximate initial pixel intensities of several pixels about an area.
These interpolation methods are extremely accurate and generate diagnostic quality magnified images. Unfortunately, these methods require massive amounts of processor time and therefore, in many cases, cannot be performed in real time because of processor limitation. For this reason high quality bicubic image magnification is not generally available.
One way to speed up calculations is to provide special hardware which is specifically designed to perform specific calculations. For example, many image processing systems include special hardware to perform either one or two dimensional high speed convolution filtering and linear or bilinear interpolation processes required in many imaging application. Unfortunately, hardware solutions have not yet been provided to facilitate bicubic interpolation.
Therefore, it would be advantageous to have a method and an apparatus which can be used with existing hardware to facilitate image magnification wherein resulting magnified images are of a quality which is essentially identical to the quality achievable using bicubic interpolation and wherein the method facilitates real time magnification.
BRIEF SUMMARY OF THE INVENTION
It has been recognized that specific hardware which already exists in many imaging work stations can be used to imitate bicubic interpolation thereby providing extremely accurate image magnification. Importantly, while the functions and calculations performed by the hardware are extremely computationally intensive, the special hardware can perform the functions and calculations extremely quickly thereby giving the appearance of real time zooming or magnification.
Generally, the hardware includes a convolver, an interpolator, and a processor for identifying convolver coefficients. After the coefficients are identified, the convolver convolves pixel intensities for each pixel in the original image region to be magnified thereby generating at least two intermediate values. The interpolator interpolates the intermediate values to generate output pixel intensities.
Specifically, according to a one dimensional method of zooming, the invention includes a method and apparatus to be used with a digital imaging system to facilitate magnification of a region of interest on an initial digital image wherein the region of interest includes a plurality of pixels of interest, adjacent halves of adjacent pixels of interest forming interpixel intervals of interest. Each pixel of interest is characterized by a pixel intensity. The system includes convolution filter and interpolation hardware. The method comprises the steps of, for each interpixel interval of interest, identifying adjacent pixels, for each adjacent pixel, identifying a convolution window and for each window, determining a convolution filter coefficient for each pixel in the window, convoluting the pixel intensities of the pixels in each window using the coefficients to generate an intermediate value, and interpolating the intermediate values to generate interpolated pixel intensities, the interpolated pixel intensities together forming an interpolated image.
Preferably, according to the one-dimensional method, the initial pixel array is arranged in y columns and x rows an
Deaven David M.
Mussack Christopher J.
Zavaljevski Aleksander
Boudreau Leo
Cabou Christian G.
General Electric Company
Patel Kanji
Quarles & Brady LLP
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