FILTERS FOR SPECTRAL ANALYSIS DATA

• US 20120323533A1
• Filed: 08/28/2012
• Published: 12/20/2012
• Est. Priority Date: 03/03/2010
• Status: Abandoned Application

• Current Assignee: Battelle Memorial Institute
• Original Assignee: Battelle Memorial Institute
• Inventors: Ronningen, Theodore J., Aume, Laura L., Rust, Steve, Wightman, Jennifer L.
• Application Number:
  US13/596,374
• Publication Number:
  US 20120323533A1
• Time in Patent Office: Days
• US Class Current: 702/191
• CPC Class Codes: G01J 3/28 Investigating the spectrum using colour filters G01J3/51

Abstract

Cosmic spike filters are provided, which remove noise spikes in spectral data. Spikes are eliminated by locating, smoothing and filtering the spikes using replicates to improve the accuracy of the spike detection process compared to when only one replicate is used. Cosmic spike filters are also provided that combine a data collection approach and a statistical approach to remove cosmic spike noise from the collected signal without distorting the true signal. Still further, a statistical approach is provided to identify and remove negative peaks from a spectrum, where the negative peaks are caused by bad pixels in a charge coupled device.

Claims

1. A method of locating and removing spike noise from spectral data comprising:

collecting a plurality of replicates of spectral data, wherein;

each collected replicate comprises a plurality of pixels, each pixel having an associated intensity value; and

the plurality of replicates represents discrete instances of spectral data obtained sequentially, from a sample under interrogation;

performing replicate processing to condition the intensity values of the collected replicates for subsequent pixel processing and spike noise filtering;

performing pixel processing to identify outliers corresponding to noise spikes within the plurality of replicates by collectively evaluating the conditioned intensity values of the plurality of replicates as a function of pixel position, wherein performing pixel processing further includes transforming the replicates by adjusting the intensity values of the plurality of replicates based upon a predetermined function; and

performing spike noise filtering by removing each identified outlier from its replicate and by replacing the removed outlier value with a computed approximation of an intensity value of a noise-free signal at that removed outlier pixel position.

2. The method according to claim 1, wherein collecting a plurality of replicates comprises collecting raw pixel data from a charge coupled device at least eight consecutive times from the sample under interrogation.

3. The method according to claim 1, wherein performing replicate processing to condition the intensity values of the collected replicates comprises:

performing a compensation for pixels values that are less than or equal to zero, by compensating the pixel value to a logarithm of a predetermined number greater than zero.

4. The method according to claim 1, wherein performing replicate processing to condition the intensity values of the collected replicates comprises processing the plurality of replicates until each replicate is processed by:

selecting a next replicate from the plurality of replicates;

selecting a subset of pixel positions for the selected replicate; and

performing compensation of the subset of pixel positions to compensate for pixel intensity values that are invalid for subsequent pixel processing and spike noise filtering.

5. The method according to claim 1, wherein performing pixel processing to identify outliers further comprises:

fitting a first model to the transformed replicates that models each pixel location as a function of replicate and adjusted intensity value;

computing for each pixel, a residual for each replicate; and

identifying whether an outlier exists for each pixel by;

identifying the replicate having the maximum residual value for each pixel location; and

declaring the pixel location for the replicate having the maximum residual value at each pixel location an outlier if the value of that maximum residual is greater than a predetermined threshold.

6. The method according to claim 5, further comprising:

replacing each identified outlier with a predicted value of that pixel position from the first model.

7. The method according to claim 1, wherein performing pixel processing to identify outliers further comprises:

performing at least two iterations of pixel processing by;

fitting a first model to the transformed replicates that models each pixel location as a function of replicate and adjusted intensity value;

computing for each pixel, a residual for each replicate; and

identifying whether an outlier exists for each pixel by;

identifying the replicate having the maximum residual value for each pixel location; and

declaring the pixel location for the replicate having the maximum residual value at each pixel location as being an outlier if the value of that maximum residual is greater than a predetermined threshold.

8. The method according to claim 1, wherein:

performing replicate processing comprises performing a compensation for pixels values that are less than or equal to zero, by compensating the pixel value greater than zero; and

performing pixel processing to identify outliers comprises performing pixel adjustments by computing the logarithm of the intensity values of each replicate.

9. The method according to claim 8, wherein performing pixel adjustments further comprises mean centering by subtracting the mean logarithm of the intensity from the logarithm of the intensity for each pixel location of each replicate.

10. The method according to claim 8, wherein performing pixel processing further comprises:

fitting a first model to the transformed replicates that linearly models each pixel location as a function of replicate and adjusted intensity value;

computing for each pixel, a residual for each replicate; and

identifying whether an outlier exists for each pixel by;

identifying the replicate having the maximum residual value for each pixel location; and

declaring the pixel location for the replicate having the maximum residual value at each pixel location as being an outlier if the value of that maximum residual is greater than a predetermined threshold.

11. The method according to claim 10, wherein declaring the pixel location for the replicate having the maximum residual value at each pixel location as being an outlier if the value of that maximum residual is greater than a predetermined threshold comprises determining whether the statistical p-value for the maximum residual is greater than a predetermined threshold.

12. The method according to claim 1, wherein performing spike noise filtering comprises:

computing a second function over the replicates;

fitting a final model that models each pixel location as a function of replicate and intensity value with identified outliers removed to replace identified outliers with the computed approximation of an intensity value of a noise free signal at that removed outlier pixel position; and

transforming each pixel of each replicate by performing the inverse of pixel adjustment processing during pixel processing.

13. The method according to claim 12, wherein transforming each replicate comprises replacing identified outliers with a predicted intensity value of that pixel number from the final model.

14. The method according to claim 12, wherein:

performing replicate processing comprises performing a compensation for pixels values that are less than or equal to zero, by compensating the pixel value greater than zero;

performing pixel processing to identify outliers comprises performing pixel adjustments by computing the logarithm of the intensity values of each replicate and by performing mean centering by subtracting the mean logarithm of the intensity from the log(intensity) for each pixel location of each replicate; and

transforming each pixel comprises adding the mean logarithm of the intensity that was subtracted out and exponentiating the result.

15. A method of locating and removing noise spikes from spectral data comprising:

obtaining a first raw replicate of spectral data, wherein the first raw replicate represents a first instance of spectral data obtained from a sample under investigation;

obtaining a second raw replicate of spectral data, wherein the second raw replicate represents a second instance of spectral data obtained from the sample under investigation;

computing a first smoothed replicate from the first raw replicate;

computing a second smoothed replicate from the second raw replicate;

determining whether at least one noise spike is present in at least one of the first raw replicate and the second raw replicate by comparing select ones of;

the first raw replicate, the first smoothed replicate, the second raw replicate and the second smoothed replicate;

eliminating the effect of each located noise spike in the first raw replicate and the second raw replicate by locally re-smoothing the corresponding replicate around each located spike; and

consolidating the first raw replicate and the second raw replicate into a single spectrum that is free of cosmic spikes.

16. The method according to claim 15, wherein eliminating the effect of each located spike in the first raw replicate and the second raw replicate comprises:

forming standardized residuals between the first raw replicate and the first smoothed replicate;

identifying pixel numbers corresponding to potential outliers in the first raw replicate where a standardized residual value exceeds a predetermined cutoff value;

comparing the first raw replicate with the second raw replicate at least at the identified pixel numbers to identify a true outlier where the comparison exceeds a predetermined value;

eliminating identified true outliers by re-smoothing the corresponding raw replicate without using the outlier data values.

17. The method according to claim 15, wherein determining whether at least one noise spike is present comprises:

calculating a first standardized residual for the difference between the first raw replicate and the first smoothed replicate;

declaring a potential outlier for each pixel of the first raw replicate where the computed first standardized residual satisfies a predetermined condition;

comparing the difference between the first raw replicate and the second raw replicate within a filter width of each pixel declared as a potential outlier to identify pixels as a true outlier if the difference between corresponding pixel values of the first raw replicate and the second raw replicate within the filter width exceeds a predetermined amount;

calculating a second standardized residual for the difference between the second raw replicate and the second smoothed replicate;

declaring a potential outlier for each pixel of the second raw replicate where the computed second standardized residual satisfies a predetermined condition;

comparing the difference between the second and first raw replicates within a filter width of each pixel declared as a potential outlier to identify pixels as a true outlier if the difference between corresponding pixel values of the second and first raw replicates within the filter width exceeds a predetermined amount; and

locally re-smoothing the first raw replicate and the second raw replicate without using the outlying values.

18. A method of detecting bad pixel elements in a charge coupled device comprising:

collecting a plurality of replicates of spectral data, wherein;

each collected replicate comprises a plurality of pixels, each pixel having an associated intensity value;

calculating a rolling median for each pixel of each collected replicate;

estimating a noise level of the corresponding spectrum;

identifying pixels of interest;

identifying local minima;

determining the height of a negative peak of select pixels;

identifying large negative peaks where local minima have a height greater than a predetermined threshold;

assessing negative peaks for removal; and

replacing each removed negative peak with a linear interpolation between the pixel previous to the peak and the next pixel after the peak.

19. The method according to claim 18 further comprising:

recording a most recent pixel which the values crossed a rolling median proceeding left to right; and

recording a most recent pixel which the values crossed a rolling median proceeding right to left.

20. The method according to claim 18, wherein assessing negative peaks for removal comprises:

tagging a peak for removal if;

the minimum height at the width of two and four pixels is less than one-half of the height of the negative peak;

the pixel is a local minimum within ten pixels, and

the width of the negative peak is less than a specified threshold.