Claims
- 1. A method of using an image mask to process optical data, the method comprising the steps of:
(a) providing image data from an area of a tissue sample; (b) identifying a subset of said image data using at least one image mask; (c) identifying one or more regions of said tissue sample from which said subset was obtained; and (d) processing optical data from said one or more regions.
- 2. The method of claim 1, wherein said optical data is spectral data.
- 3. The method of claim 1, wherein said processing step comprises filtering spectral data for use in a tissue classification scheme.
- 4. The method of claim 3, wherein said processing step comprises disqualifying data corresponding to the one or more regions identified in step (c) from use in said tissue classification scheme.
- 5. The method of claim 3, wherein said processing step comprises classifying the one or more regions identified in step (c) as indeterminate.
- 6. The method of claim 3, wherein said tissue classification scheme comprises a principal component analysis method.
- 7. The method of claim 3, wherein said tissue classification scheme comprises a feature coordinate extraction method.
- 8. The method of claim 3, wherein said tissue classification scheme comprises a principal component analysis method and a feature coordinate extraction method.
- 9. The method of claim 1, wherein said processing step comprises determining a percent mask coverage for each of the one or more regions identified in step (c).
- 10. The method of claim 9, wherein said processing step comprises applying a weighting factor according to said percent mask coverage.
- 11. The method of claim 1, wherein said at least one image mask comprises a binary image mask.
- 12. The method of claim 1, wherein said at least one image mask identifies a set of pixels.
- 13. The method of claim 1, wherein said at least one image mask comprises an obstruction mask.
- 14. The method of claim 13, wherein said obstruction mask is selected from the group consisting of a blood mask, a mucus mask, a speculum mask, and a pooled fluid and foam mask.
- 15. The method of claim 1, wherein said first identifying step comprises thresholding an initial mask and performing a binary component analysis.
- 16. The method of claim 1, wherein said at least one image mask comprises a glare mask.
- 17. The method of claim 16, wherein said first identifying step comprises dividing an image into a plurality of blocks, determining a histogram corresponding to each of the blocks, and computing one or more thresholds for each of the blocks based on its corresponding histogram.
- 18. The method of claim 1, wherein said at least one image mask comprises at least one of the group consisting of an os mask, a smoke tube mask, a vaginal wall mask, and a region-of-interest mask.
- 19. The method of claim 18, wherein said first identifying step comprises determining a gradient image, using said gradient image to determine a skeletonized image, and performing edge linking and edge extension using said skeletonized image.
- 20. The method of claim 18, wherein said first identifying step comprises thresholding a red channel component of said image data.
- 21. The method of claim 1, wherein said at least one image mask comprises at least three of the group consisting of a blood mask, a mucus mask, a speculum mask, a pooled fluid and foam mask, a glare mask, an os mask, a smoke tube mask, a vaginal wall mask, and a region-of-interest mask.
- 22. The method of claim 1, wherein said at least one image mask comprises at least six of the group consisting of a blood mask, a mucus mask, a speculum mask, a pooled fluid and foam mask, a glare mask, an os mask, a smoke tube mask, a vaginal wall mask, and a region-of-interest mask.
- 23. The method of claim 1, wherein said at least one image mask comprises the group consisting of a blood mask, a mucus mask, a speculum mask, a pooled fluid and foam mask, a glare mask, an os mask, a smoke tube mask, a vaginal wall mask, and a region-of-interest mask.
RELATED APPLICATIONS
[0001] This application is related to the following commonly-owned applications: Attorney Docket No. MDS-035, entitled, “Methods and Apparatus for Characterization of Tissue Samples”; Attorney Docket No. MDS-035A, entitled, “Methods and Apparatus for Displaying Diagnostic Data”; Attorney Docket No. MDS-035B, entitled, “Methods and Apparatus for Visually Enhancing Images”; Attorney Docket No. MDS-035D, entitled, “Methods and Apparatus for Characterization of Tissue Samples”; Attorney Docket No. MDS-035F, entitled, “Methods and Apparatus for Processing Spectral Data for Use in Tissue Characterization”; Attorney Docket No. MDS-035G, entitled, “Methods and Apparatus for Evaluating Image Focus”; and MDS-035H, entitled, “Methods and Apparatus for Calibrating Spectral Data,” all of which are filed on even date herewith.