Patent Application
20050276512
This invention relates to image processing, and, in particular, the selective use of deconvolution to reduce crosstalk between features of an image. By selecting relevant areas for deconvolution, a process which typically involves intensive calculations, the present invention can greatly reduce the calculation effort needed to provide superior image quality.
U.S. Pat. No. 6,477,273, incorporated herein by reference, discloses methods of centroid integration of an image. U.S. Pat. No. 6,633,669, incorporated herein by reference, discloses methods of autogrid analysis of an image. U.S. patent application Ser. No. 09/917,545, incorporated herein by reference, discloses methods of autothresholding of an image.
Briefly, the present invention provides a method to select areas of an image for deconvolution comprising the steps of: a) providing an image comprising a plurality of features, wherein each feature is associated with at least one value (v); b) identifying a test feature which is a high-value feature adjacent to a known low-value zone of the image, wherein the test feature has a tail ratio (rt), which is the ratio of the value of the test feature (vt) to the value of the adjacent low-value zone of the image (vo); c) calculating a threshold value t which is a function of tail ratio (rt) of the test feature; and d) identifying selected areas of the image, the selected areas being those where the ratio of values (v) between adjacent features is greater than said threshold value (T(rt)). The image typically comprises features arranged in a grid. Typically, a pseudo-image is formed by autogrid analysis. Typically, step b) additionally comprises subtracting a background constant from both the value of the test feature (vt) and the value of the adjacent low-value zone of the image (vo) before calculating the tail ratio (rt). The background constant may optionally be taken to be the value of a (vb) of a low-value zone of the image which is sufficiently distant from any feature as to avoid any tail effect, which may optionally be a low-value zone of the image which is at least twice as distant from any feature as the average distance between features. Typically, threshold value (T(rt)) is a multiple of tail ratio (rt) of said test feature. Typically, the method of the present invention additionally comprises the step of deconvolving the selected areas of the image.
In another aspect, the present invention provides a system for selecting areas of an image for deconvolution, the system comprising: a) an image device for providing a digitized image; b) a data storage device; and c) a central processing unit for receiving the digitized image from the image device and which can write to and read from the data storage device, the central processing unit being programmed to:
It is an advantage of the present invention to provide a method to reduce the calculation effort necessary to derive high quality data from an image.
The present invention provides a method to select areas of an image for deconvolution. Any suitable method of deconvolution known in the art may be used, including iterative and blind methods. Iterative methods include Richardson-Lucy and Iterative Constrained Tikhovan-Miller methods. Blind methods include Weiner Filtering, Simulated Annealing and Maximum Likelihood Estimators methods. Deconvolution may reduce cross-talk between features in an image, such as the false lightening of a relatively dark feature due to its proximity to a light feature.
The method of selection comprises the steps of: a) providing an image comprising a plurality of features, wherein each feature is associated with at least one value (v); b) identifying a test feature which is a high-value feature adjacent to a known low-value zone of the image, wherein the test feature has a tail ratio (rt), which is the ratio of the value of the test feature (vt) to the value of the adjacent low-value zone of the image (vo); c) calculating a threshold value t which is a function of tail ratio (rt) of the test feature; and d) identifying selected areas of the image, the selected areas being those where the ratio of values (v) between adjacent features is greater than said threshold value (T(rt)). Typically, one or more steps are automated. More typically, all steps are automated.
The step of providing an image may be accomplished by any suitable method. Typically, this step is automated. The image may be collected by use of a video camera, digital camera, photochemical camera, microscope, telescope, visual scanning system, probe scanning system, or other sensing apparatus which produces data points in a two-dimensional array. Typically, the target image is expected to be an image containing distinct features, which, however, may additionally contain noise. Typically the features are arranged in a grid comprising rows and columns. As used herein, “column” will be used to indicate general alignment of the features in one direction, and “row” to indicate general alignment of the features in a direction generally orthogonal to the columns. It will be understood that which direction is the column and which the row is entirely arbitrary, so no significance should be attached to the use of one term over the other, and that the rows and columns may not be entirely straight. Alternately, a grid may comprise some other repeating geometrical arrangement of features, such as a triangular or hexagonal arrangement. Alternately, the features may be arranged in no predetermined pattern, such as in an astronomical image. If the image is not initially created in digital form by the image capturing or creating equipment, the image is typically digitized into pixels. Typically, the methods described herein are accomplished with use of a central processing unit or computer.
The image may be subjected to centroid integration and autogrid analysis, as described in U.S. Pat. Nos. 6,477,273 and 6,633,669, incorporated herein by reference, prior to further analysis. Each feature may be assigned an integrated intensity as provided therein as its “value,” or may be assigned a value by any other suitable method, which might include selection of local maxima as feature values, or the like. A pseudo-image, formed by autogrid analysis, may be generated.
As used herein, “high-value” and “low-value” are used in reference to bright and dark features in a photographic image. It will be understood that the terms “high-value”, “low-value” and “value” may be applied to any characteristic which might be represented in an image, including without limitation color values, x-ray transmission values, radio wave emission values, and the like, depending on the nature of the image and the apparatus used to collect the image. Typically, “high-value” would refer to a characteristic that would tend to create cross-talk in adjacent “low-value” features, depending on the nature of the image collection apparatus.
The step of identifying a test feature may be accomplished by any suitable method. Typically, this step is automated. The test feature is a high-value feature adjacent to a known low-value zone of the image. The low-value zone may be a low-value feature or an area known to be low-value, such as an edge area or other area known to be outside the area where features are expected. In one embodiment, features making up the edge of an expected grid of features are examined and a bright edge feature selected as the test feature. The feature selected as the test feature may be the highest-value of a set of candidates or may be the first examined which surpasses a pre-selected threshold. In another embodiment, the object to be imaged is provided with adjacent high-value and low-value features to serve as reference points.
A tail ratio (rt) is calculated by dividing the value of the test feature (vt) by the value of the adjacent low-value zone of the image (vo). Typically, a background constant is subtracted from both the value of the test feature (vt) and the value of the adjacent low-value zone of the image (vo) before calculating the tail ratio (rt). The background constant may be determined by any suitable method. The background constant may be taken to be the value of a (vb) of a low-value zone of the image which is sufficiently distant from any feature as to avoid any tail effect. Where the features are arranged in a grid, the distant low-value zone is typically at least twice as distant from any feature as the average distance between features. Alternately, the background constant may be a fixed value, determined a priori to be suitable for a given apparatus.
A threshold value t is calculated, which is a function of the tail ratio (rt) of the test feature. Any suitable function may be used, including functions that are arithmetic, logarithmic, exponential, trigonometric, and the like. Typically the threshold value (T(rt)) is simply a multiple of tail ratio (rt), i.e., T(rt)=A×rt, where A is any suitable number but most typically between 2 and 20.
Threshold value t is then used to identify selected areas of the image by any suitable method. Typically, this step is automated. Most typically, the selected areas are those where the ratio of values (v) between adjacent features is greater than said threshold value (T(rt)).
This invention is useful in the automated reading of optical information, particularly in the automated reading of a matrix of sample points on a tray, slide, or suchlike, which may be comprised in automated analytical processes like DNA detection or typing. Alternately, this invention may be useful in astronomy, medical imaging, real-time image analysis, and the like. In particular, this invention is useful in reducing spatial cross-talk by deconvolution of the image without undue calculation.
Objects and advantages of this invention are further illustrated by the following example, but the particular order and details of method steps recited in these examples, as well as other conditions and details, should not be construed to unduly limit this invention.
The subject image used in this example is shown in
The image was first subjected to autogrid analysis, as described in U.S. Pat. Nos. 6,477,273 and 6,633,669, incorporated herein by reference, including the “flexing” described in U.S. Pat. No. 6,633,669, to create the analysis grid depicted in
A bright edge feature at column 1, row E, was chosen as the test feature.
The threshold value was taken to be 10 times the tail ratio, or 0.168. The goal is thus to select features having an intensity (b) less than 10 times as bright as the expected contribution from an adjacent bright feature; that is, less than 10 times the brightness of the adjacent feature (a) times the tail ratio. This condition can be expressed in Formula I: b<a×10×(tail ratio), or b<a×(threshold).
The integrated intensity values and the threshold were converted to logs in order to simplify successive operations. Table II contains the natural log of the integrated intensity values reported in Table I for each column and row position. The value of ln(threshold) was −1.78. Formula I is expressed in terms of logarithms in Formula II: ln(b)<ln(a)+ln(threshold), which rearranges to −ln(threshold)<ln(a)−ln(b). Taking the absolute value of the brightness difference so as to detect both bright/dark and dark/bright transitions, Formula II becomes Formula III: −ln(threshold)<|ln(a)−ln(b)|.
Table III reports the absolute value of the differences between adjacent values in Table II in the x direction, i.e., |ln(a)−ln(b)|. Table III therefore contains nine columns and nine rows. The values in Table III were normalized to 1.000 by dividing by the maximum value in the table, 2.911. The normalized values are reported in Table IV. The −ln(threshold) value of 1.78 was normalized to 1.78/2.911=0.61. The normalized threshold was applied to Table IV to produce Table V, which reports a 0 for values less than −ln(threshold) or 0.61 and a 1 for values greater than −ln(threshold) or 0.61.
Table VI reports the absolute value of the differences between adjacent values in Table II in the y direction, i.e., |ln(a)−ln(b)|. Table VI therefore contains ten columns and eight rows. The values in Table VI were normalized to 1.000 by dividing by the maximum value in the table, 3.2751. The normalized values are reported in Table VII. The −ln(threshold) value of 1.78 was normalized to 1.78/3.2751=0.54. The normalized threshold was applied to Table VII to produce Table VII, which reports a 0 for values less than −ln(threshold) or 0.54 and a 1 for values greater than −ln(threshold) or 0.54.
Table V was convolved with the kernel:
to create a 9 by 10 matrix, Table IX, where non-zero entries indicate bright-to-dark or dark-to-bright transitions in the x direction.
Table VIII was convolved with kernel:
to create a 9 by 10 matrix, Table X, where non-zero entries indicate bright-to-dark-to-bright transitions in the y direction.
The matrices represented by Tables IX and X were added, resulting in the matrix reported as Table XI.
Four rectangular regions were selected for deconvolution encompassing all of the non-zero values in Table XI (A1:B3, D5:F7, H1:I3, I9:I10). The selected regions included 23 out of 90 features, saving at least about 74% of the calculation effort that would have been involved in deconvolution of the entire image, and possibly much more, since many methods of deconvolution provide that the extent of the calculation effort rises exponentially with the size of the region analyzed.
Various modifications and alterations of this invention will become apparent to those skilled in the art without departing from the scope and principles of this invention, and it should be understood that this invention is not to be unduly limited to the illustrative embodiments set forth hereinabove.
