System and methods for generating a brightfield image using fluorescent images

1. A method for generating a brightfield type image that resembles a brightfield staining protocol comprising the steps of:

acquiring image data of two or more fluorescent images of a fixed area on a biological sample;

analyzing the image data utilizing, at least in part, featured based information or pixel intensity data information to generate mapping parameters wherein said mapping parameters comprises a nonlinear estimation model;

applying said mapping parameters to the fluorescent images;

transforming the two or more fluorescent imaging into a brightfield color space; and

generating a brightfield type image.

where the nonlinear estimation model is defined as:

R= 255 exp(− a[ Dye1]* z[ Dye1]− a[ Dye2] z[ Dye2]−. . . a[ Dyen] z[ Dyen])

G= 255 exp(− b[ Dye1]* z[ Dye1]− b[ Dye2] z[ Dye2]−. . . )

B =255 exp(− c[ Dye1]* z[ Dye1]− c[ Dye2] z[ Dye2]−. . . )

wherein R, G, and B are resulting red, green and blue pixel values in the brightfield type image;

z is a scaling coefficient for a fluorescent dye quantities observed at a given pixel location; and

a, b, and c are the extinction coefficients corresponding to the brightfield color space, and wherein the triples, a[Dyen], b[Dyen], c[Dyen], are a constant times the extinction coefficients of the nth dye in the virtual stain as defined using a preselected or desired color.

2. The method of claim 1 wherein the constants [Dyen] are chosen such that:

min(exp(− a[ Dyen]* z[ Dyen]),exp(− b[ Dyen]* z[ Dyen]),exp(− c[ Dyen]* z[ Dyen]))=1/255.

3. The method of claim 1 further comprising applying a sharpening transformation correction to the brightfield type image.

4. The method of claim 3 wherein said sharpening transformation correction comprising a convolution filter whose kernel is the matrix:

[

-

0.25

-

0.25

-

0.25

-

0.25

3.00

-

0.25

-

0.25

-

0.25

-

0.25

]

.

5. The method of claim 1 wherein the brightfield type image corresponds to an immunostained type image having a red, green, and blue three channel color space.

6. The method of claim 1 wherein at least one image of the two or more fluorescent images is of autofluorescence.

7. The method of claim 1 wherein the acquiring a brightfield image step comprises the steps of sequentially staining the biological sample with two or more histochemical or immunohistochemical stains.

8. The method of claim 1 wherein the feature based information comprises one or more features selected from a group consisting of nuclei, epithelia, and stroma.

9. The method of claim 1 further comprising the step of applying the mapping parameters to two or more fluorescent images of a second fixed area wherein the second fixed area is from the same or a different biological sample.

10. The method of claim 1 further comprising the step of pathological diagnostics using said brightfield type image.

11. The method of claim 10 wherein the pathological diagnostics is for cancer.

12. The method of claim 1 further comprising the step of quantitative analysis using said brightfield type image.

13. The method of claim 12 wherein the step of quantitative analysis comprises identifying molecular pathways as a function of one or more morphological structures selected from a group consisting of nuclei, epithelia, and stroma.

14. An image analysis system for generating a brightfield type image that resembles a brightfield staining protocol of a biological sample, using fluorescent images comprising:

a digital imaging device adapted to acquire two or more fluorescent images of a fixed area on a biological sample;

a processing device configured to;

analyzing the image data utilizing, at least in part, featured based information or pixel intensity data information to generate mapping parameters wherein said mapping parameters comprises a nonliner estimation model;

applying said mapping parameters to the fluorescent images;

transforming the two or more fluorescent imaging into a brightfield color space; and

generating a brightfield type image; and

a display device for displaying said brightfield image;

where the nonlinear estimation model is defined as:

R = 255 exp(− a[ Dye1]* z[ Dye1]− a[ Dye2] z[ Dye2]−. . . a[ Dyen] z[ Dyen])

G = 255 exp(− b[ Dye1]* z[ Dye1]− b[ Dye2] z[ Dye2]−. . . )

B = 255 exp(− c[ Dye1]* z[ Dye1]− c[ Dye2] z[ Dye2]−. . . )

wherein R, G, and B are resulting red, green and blue pixel values in the brightfield type image.

z is a scaling coefficient for a fluorescent dye quantities observed at a given pixel location; and

a, b, and c are the extinction coefficients corresponding to the brightfield color space, and wherein the triples, a[Dyen], b[Dyen], c[Dyen], are a constant times the extinction coefficients of the nth dye in the virtual stain as defined using a preselected or desired color.

15. The system of claim 14 wherein the constants [Dyen] are chosen such that:

min(exp(− a[ Dyen]* z[ Dyen]),exp(− b[ Dyen]* z[ Dyen]),exp(− c[ Dyen]* z[ Dyen]))=1/255.

16. The system of claim 14 further comprising applying a sharpening transformation correction to the brightfield type image.

17. The system of claim 16 wherein said sharpening transformation correction comprising a convolution filter whose kernel is the matrix:

[

-

0.25

-

0.25

-

0.25

-

0.25

3.00

-

0.25

-

0.25

-

0.25

-

0.25

]

.

18. The system of claim 14 wherein the processing device is further configured to store mapping parameters from one or more previously analyzed biological samples.