Image analysis – Applications – Biomedical applications
Reexamination Certificate
1998-05-28
2002-06-11
Au, Amelia M. (Department: 2623)
Image analysis
Applications
Biomedical applications
C382S205000, C382S265000, C382S308000, C378S037000
Reexamination Certificate
active
06404908
ABSTRACT:
FIELD OF THE INVENTION
The present invention relates to the field of computer aided analysis of medical images. In particular, the present invention relates to a fast method for detecting lines in medical images.
BACKGROUND OF THE INVENTION
Line detection is an important first step in many medical image processing algorithms. For example, line detection is an important early step of the algorithm disclosed in U.S. patent application Ser. No. 08/676,660, entitled “Method and Apparatus for Fast Detection of Spiculated Lesions in Digital Mammograms,” filed Jul. 19, 1996, the contents of which are hereby incorporated by reference into the present application. Generally speaking, if the execution time of the line detection step can be shortened, then the execution time of the overall medical image processing algorithm employing that line detection step can be shortened.
In order to clearly illustrate the features and advantages of the preferred embodiments, the present disclosure will describe the line detection algorithms of both the prior art and the preferred embodiments in the context of the computer-assisted diagnosis system of U.S. patent application Ser. No. 08/676,660, supra. Importantly, however, the scope of the preferred embodiments is not so limited, the features and advantages of the preferred embodiments being applicable to a variety of image processing applications.
FIG. 1
shows steps performed by a computer-assisted diagnosis unit similar to that described in U.S. patent application Ser. No. 08/676,660, which is adapted to detect abnormal spiculations or lesions in digital mammograms. At step
102
, an x-ray mammogram is scanned in and digitized into a digital mammogram. The digital mammogram may be, for example, a 4000×5000 array of 12-bit gray scale pixel values. Such a digital mammogram would generally correspond to a typical 8″×10″ x-ray mammogram which has been digitized at 50 microns (0.05 mm) per pixel.
At step
104
, which is generally an optional step, the digital mammogram image is locally averaged, using steps known in the art, down to a smaller size corresponding, for example, to a 200 micron (0.2 mm) spatial resolution. The resulting digital mammogram image that is processed by subsequent steps is thus approximately 1000×1250 pixels. As is known in the art, a digital mammogram may be processed at different resolutions depending on the type of features being detected. If, for example, the scale of interest is near the order of magnitude 1 mm-10 mm, i.e., if lines on the order of 1 mm-10 mm are being detected, it is neither efficient nor necessary to process a full 50-micron (0.05 mm) resolution digital mammogram. Instead, the digital mammogram is processed at a lesser resolution such as 200 microns (0.2 mm) per pixel.
Generally speaking, it is to be appreciated that the advantages and features of the preferred embodiments disclosed infra are applicable independent of the size and spatial resolution of the digital mammogram image that is processed. Nevertheless, for clarity of disclosure, and without limiting the scope of the preferred embodiments, the digital mammogram images in the present disclosure, which will be denoted by the symbol I, will be M×N arrays of 12-bit gray scale pixel values, with M and N having exemplary values of 1000 and 1250, respectively.
At step
106
, line and direction detection is performed on the digital mammogram image I. At this step, an M×N line image L(i,j) and an M×N direction image &thgr;
max
(i,j) are generated from the digital mammogram image I. The M×N line image L(i,j) generated at step
106
comprises, for each pixel (i,j), line information in the form of a “1” if that pixel has a line passing through it, and a “0” otherwise. The M×N direction image &thgr;
max
(i,j) comprises, for those pixels (i,j) having a line image value of “1”, the estimated direction of the tangent to the line passing through the pixel (i,j). Alternatively, of course, the direction image &thgr;
max
(i,j) may be adjusted by 90 degrees to correspond to the direction orthogonal to the line passing through the pixel (i,j).
At step
108
, information in the line and direction images is processed for determining the locations and relative priority of spiculations in the digital mammogram image I. The early detection of spiculated lesions (“spiculations”) in mammograms is of particular importance because a spiculated breast tumor has a relatively high probability of being malignant.
Finally, at step
110
, the locations and relative priorities of suspicious spiculated lesions are output to a display device for viewing by a radiologist, thus drawing his or her attention to those areas. The radiologist may then closely examine the corresponding locations on the actual film x-ray mammogram. In this manner, the possibility of missed diagnosis due to human error is reduced.
One of the desired characteristics of a spiculation-detecting CAD system is high speed to allow processing of more x-ray mammograms in less time. As indicated by the steps of
FIG. 1
, if the execution time of the line and direction detection step
106
can be shortened, then the execution time of the overall mammogram spiculation detection algorithm can be shortened.
A first prior art method for generating line and direction images is generally disclosed in Gonzales and Wintz,
Digital Image Processing
(1987) at 333-34. This approach uses banks of filters, each filter being “tuned” to detect lines in a certain direction. Generally speaking, this “tuning” is achieved by making each filter kernel resemble a second-order directional derivative operator in that direction. Each filter kernel is separately convolved with the digital mammogram image I. Then, at each pixel (i,j), line orientation can be estimated by selecting the filter having the highest output at (i,j), and line magnitude may be estimated from that output and other filter outputs. The method can be generalized to lines having pixel widths greater than 1 in a multiscale representation shown in Daugman, “Complete Discrete 2-D Gabor Transforms by Neural Networks for Image Analysis and Compression,”
IEEE Trans. ASSP
, Vol. 36, pp. 1169-79 (1988).
The above filter-bank algorithms are computationally intensive, generally requiring a separate convolution operation for each orientation-selective filter in the filter bank. Additionally, the accuracy of the angle estimate depends on the number of filters in the filter bank, and thus there is an implicit tradeoff between the size of the filter bank (and thus total computational cost) and the accuracy of angle estimation.
A second prior art method of generating line and direction images is described in Karssemeijer, “Recognition of Stellate Lesions in Digital Mammograms,”
Digital Mammography: Proceedings of the
2
nd International Workshop on Digital Mammography
, York, England, (Jul. 10-12, 1994) at 211-19, and in Karssemeijer, “Detection of Stellate Distortions in Mammograms using Scale Space Operators,”
Information Processing in Medical Imaging
335-46 (Bizais et al., eds. 1995) at 335-46. A mathematical foundation for the Karssemeijer approach is found in Koenderink and van Doom, “Generic Neighborhood Operators,”
IEEE Transactions on Pattern Analysis and Machine Intelligence
, Vol. 14, No. 6 (June 1992) at 597-605. The contents of each of the above two Karssemeijer references and the above Koenderink reference are hereby incorporated by reference into the present application.
The Karssemeijer algorithm uses scale space theory to provide an accurate and more efficient method of line detection relative to the filter-bank method. More precisely, at a given level of spatial scale &sgr;, Karssemeijer requires the convolution of only three kernels with the digital mammogram image I, the angle estimation at a pixel (i,j) then being derived as a trigonometric function of the three convolution results at (i,j) .
FIG. 2
shows steps for computing line and direction images in accordance with the Karssemeijer algorithm. At step
Roehrig Jimmy R.
Schneider Alexander C.
Au Amelia M.
Dastouri Mehrdad
Pennie & Edmonds LLP
R2 Technology, Inc.
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