Adaptive coherent optical processing method and apparatus for recognizing and counting objects

• US 4,139,303 A
• Filed: 01/21/1977
• Issued: 02/13/1979
• Est. Priority Date: 01/21/1977
• Status: Expired due to Term

• Current Assignee: Board of Regents of The University of Washington (University of Washington)
• Original Assignee: Board of Regents of The University of Washington (University of Washington)
• Inventors: Lee, Charles K., Carlson, F. Paul
• Primary Examiner(s): Corbin, John K.
• Assistant Examiner(s): Punter, Wm. H.
• Application Number:
  US05/761,365
• Time in Patent Office: 753 Days
• Field of Search: 356/39, 356/102-104, 250/222 PC, 250/550, 250/574, 340/146.3 F, 340/146.3 P, 340/146.3 Q, 340/146.3 AC, 340/146.3 CA, 235/92 PC, 350/162 SF
• US Class Current: 356/39
• CPC Class Codes: G01N 15/0211 Investigating a scatter or diffraction pattern
• CPC Class Codes: G01N 2021/4716 Using a ring of sensors, or a combination of diaphragm and sensors; Annular sensor
• CPC Class Codes: G06V 10/88 Image or video recognition using optical means, e.g. reference filters, holographic masks, frequency domain filters or spatial domain filters

Abstract

Recognizing and counting geometrically distant objects, such as objects of a particular morphological type (e.g., reticulated red blood cells), located in a field of objects of varying types is disclosed. Coherent light is directed toward a monolayer of objects of various types. The light scattered by the objects is collected by a collecting lens and forms a composite Fourier spectrum at the focal plane of the lens. The Fourier spectrum is selectively analyzed on the basis that each object creates a unique portion of the composite Fourier spectrum, and that a family of objects that are geometrically similar have additive spectrums, when their population is large, randomly located, and nonoverlapping. The analysis is performed by making intensity measurements at radial points in the Fourier plane, weighting the measurements, and summing the result. The radial points and weighting factors are determined using regression techniques.

Claims

1. A method of recognizing and counting randomly sized, randomly located and randomly oriented objects of a distinct geometrical shape in a mass of objects of varying geometrical shapes, said method comprising the steps of:

arraying said mass of objects in a non-overlapping field;

passing a beam of coherent light through said non-overlapping field such that said field scatters said beam of coherent light;

positioning a Fourier transform lens such that the lens collects light scattered by said non-overlapping field and forms a composite Fourier spectrum of said non-overlapping field;

measuring the intensity of the light at selected points in said composite Fourier spectrum; and

,

combining said light intensity measurements by nonuniformly weighting each light intensity measurement made by a predetermined weighting factor, B; and

, summing said nonuniformly weighted light intensity measurements to provide an estimated count of the number of objects of a distinct geometrical shape located in said non-overlapping field.

2. A method of recognizing and counting randomly sized, randomly located and randomly oriented objects of a distinct geometrical shape in a mass of objects of varying geometrical shapes as claimed in claim 1 wherein:

said non-overlapping field is positioned orthogonal to said beam of coherent light; and

,

said Fourier transform lens is positioned a focal length'"'"'s distance from said non-overlapping field on the side thereof remote from the side receiving said beam of coherent light, whereby said Fourier spectrum is formed a focal length'"'"'s distance from said Fourier transform lens.

3. A method of recognizing and counting randomly sized, randomly located and randomly oriented objects of a distinct geometrical shape in a mass of objects of varying geometrical shapes as claimed in claim 2 wherein:

said objects of a distinct geometrical shape produce a Fourier spectrum having hills and valleys; and

,

at least some of the selected positions at which said light intensity measurements are made are located at some of the hills and valleys of the Fourier spectrum produced by said objects of a distinct geometrical shape.

4. A method of recognizing and counting randomly sized, randomly located and randomly oriented objects of a distinct geometrical shape in a mass of objects of varying geometrical shapes as claimed in claim 3 wherein said weighting factors, B, are determined by using regression techniques based on measurements made using training non-overlapping fields wherein the number of objects of said distinct geometrical shape is known.

5. A method of recognizing and counting randomly sized, randomly located and randomly oriented objects of a distinct geometrical shape in a mass of objects of varying geometrical shapes as claimed in claim 2 wherein said selected points at which said light intensity measurements are made and said weighting factos, B, are determined by using regression techniques based on measuremens made using training non-overlapping fields wherein the number of objects of said distinct geometrical shape is known.

6. A mehod of recognizing and counting randomly sized, randomly located and randomly oriented objects of varying geometrical shapes as claimed in claim 2 wherein said weighting factors, B, are determined by using regression techniques based on measurements made using training non-overlapping fields wherein the number of objects of said distinct geometrical shape is known.

7. A method of recognizing and counting randomly sized, randomly located and randomly oriented objects of a distinct geometrical shape in a mass of objects of varying geometrical shapes as claimed in claim 1 wherein said selected points at which said light intensity measurements are made and said weighting factors, B, are determined by using regression techniques based on measurements made using training non-overlapping fields wherein the number of objects of said distinct geometrical shape is known.

8. A method of recognizing and counting randomly sized, randomly located and randomly oriented cells of a distinct morphology, such as reticulocytes, in a mass of cells of varying morphology, such as a blood sample, said method comprising the steps of:

arraying said mass of cells on a slide so as to form a non-overlapping field;

passing a beam of coherent light through said non-overlapping field of cells such that said field scatters said beam of coherent light;

positioning a Fourier transform lens such that the lens collects light scattered by said non-overlapping field of cells and forms a composite Fourier spectrum of said non-overlapping field;

measuring the intensity of the light at selected points in said composite Fourier spectrum; and

,

combining said light intensity measurements by nonuniformly weighting each light intensity measurement mode by a predetermined factor, B; and

summing said nonuniforly weighted light intensity measurements to provide an estimated count of the number of cells of said distinct morphology located in said non-overlapping field of cells.

9. A method of recognizing and counting randomly sized, randomly located and randomly oriented cells of a distinct morphology in a mass of cells of varying morphology as claimed in claim 8 wherein:

said slide containing said non-overlapping field of cells is positioned orthogonal to said beam of coherent light; and

,

said Fourier transform lens is positioned a focal length'"'"'s distance from said non-overlapping field of cells on the side thereof remote from the side receiving said beam of coherent light, whereby said Fourier spectrum is formed a focal length'"'"'s distance from said Fourier transform lens.

10. A method of recognizing and counting randomly sized, randomly located and randomly oriented cells of a distinct morphology in a mass of cells of varying morphology as claimed in claim 9 wherein:

said cells of a distinct morphology produce a Fourier spectrum having hills and valleys; and

,

at least some of the selected points at which said light intensity measurements are made are located at some of the hills and valleys of the Fourier spectrum produced by said cells of a distinct morphology.

11. A method of recognizing and counting randomly sized, randomly located and randomly oriented cells of a distinct morphology in a mass of cells of varying morphology as claimed in claim 10 wherein said weighting factors, B, are determined by using regression techniques based on measurements made using training slides wherein the number of cells of said distinct morphology is known.

12. A method of recognizing and counting randomly sized, randomly located and randomly oriented cells of a distinct morphology in a mass of cells of varying morphology as claimed in claim 9 wherein said selected points at which said light intensity measurements are made and said weighting factors, B, are determined by using regression techniques based on measurements made using training slides wherein the number of cells of said distinct morphology is known.

13. A method of recognizing and counting randomly sized, randomly located and randomly oriented cells of a distinct morphology in a mass of cells of varying morphology as claimed in claim 8 wherein said weighting factors, B, are determined by using regression techniques based on measurements made using training slides wherein the number of cells of said distinct morphology is known.

14. A method of recognizing and counting randomly sized, randomly located and randomly oriented cells of a distinct morphology in a mass of cells of varying morphology as claimed in claim 8 wherein said selected points at which said light intensity measurements are made and weighting factors, B, are determined by using regression techniques based on measurements made using training slides wherein the number of cells of said distinct morphology is known.

15. Apparatus for recognizing and counting randomly sized, randomly located and randomly oriented objects of distinct geometrical shape in a mass of objects of varying geometrical shapes, said appratus comprising:

light means for producing a collimated coherent light beam;

support means for supporting, in a non-overlapping field, a mass of objects of varying geometrical shapes in said coherent light beam;

transform lens means, mounted on the side of said support means remote from the side receiving said coherent light beam, for collecting light scattered by the mass of objects supported by said support means in a non-overlapping field and forming said collecting light into a composite Fourier spectrum of the mass of objects forming said non-overlapping field;

light detecting means mounted so as to detect the intensity of the light at selected points in the Fourier spectrum produced by said transform lens means and produce output signals having a parameter related to the intensity of the light detected at said selected points in said Fourier spectrum; and

,

combining means connected to said light detecting means for receiving said output signals having a parameter related to the intensity of the light detected by said light detecting means, nonuniformly weighting selected ones of said received output signals and summing the resultant nonuniformly weighted signals to produce a combined output signal having a parameter related to the number of objects of a distinct geometrical shape located in said non-overlapping field.

16. Apparatus for recognizing and counting randomly sized, randomly located and randomly oriented objects of a distinct geometrical shape in a mass of objects of varying geometrical shapes as claimed in claim 15 wherein said transform lens means is located a focal length'"'"'s distance from said support means and wherein said composite Fourier spectrum is formed at the focal plane of said transform lens means located on the side of said lens means remote from the side on which said support means is located.

17. Apparatus for recognizing and counting randomly sized, randomly located and randomly oriented objects of a distnct geometrical shape in a mass of objects of varying geometrical shapes as claimed in claim 16 wherein:

said transform lens means defines an optical axis;

said support means and said light detecting means lie in planes orthogonal to said optical axis; and

,

said selected points at which said light detecting means detect light in the Fourier spectrum produced by said transform lens means are located at predetermined radial positions in the plane in which said light detecting means lies.

18. Apparatus for recognizing and counting randomly sized, randomly located and randomly oriented objects of a distinct geometrical shape in a mass of objects of varying geometrical shapes as claimed in claim 17 wherein:

said light detecting means produce analog signals whose magnitude is related to the intensity of the light detected by said light detecting means;

said combining means includes analog-to-digital converting means connected to said light detcting means for converting said analog signals into digital signals; and

,

said combining means also includes calculating means connected to said analog-to-digital converting means for nonuniformly weighting said digital signals and summing the resulting nonuniformly weighted digital signals.

19. A method of determining the vector positions, X_ij, at which light intensity measuremments are to be made in a pattern recognition system for recognizing geometrically distinct objects that includes a source of coherent energy positioned so as to direct a beam of coherent energy through a mass of objects located in a monolayer field and a fourier transform lens positioned so as to collect energy scattered by the objects forming said monolayer field and form a composite fourier spectrum thereof, said method comprising the steps of:

assuming a set of vector positions x'"'"'_ij ;

making energy intensity measurements at each assumed vector position x'"'"'_ij, for each one of a training set of monolayer fields having a known parameter related to the objects to be recognized;

performing a partial F-value test using the results of said measurements for each assumed vector position, x'"'"'_ij ; and

,

testing the results of each partial F-value test to determine if the result is greater than F₁,N-k-1;

γ

where γ

is the gamma distribution point of Fisher'"'"'s F distribution of 1 and N-k-1 degrees of freedom, N is equal to the number of samples used in the training set and k is the total number of vector positions x_ij to be determined; and

,

choosing the assumed vector positions, x'"'"'_ij, to be vector positions, x_ij, at which measuremens are to be made if the result of their related partial F-value test is greater than F₁,N-k-1;

γ

.

20. A method of choosing the vector positions, x_ij, at which measurements are to be made in a pattern recognition system as claimed in claim 19 including the further step of determining if each assumed variable, x'"'"'_ij, that has a partial F-value test result greater than F₁,N-k-1;

γ

, has a large probability of having a null value and rejecting the assumed variables, x'"'"'_ij, that have a large probability of having a null value prior to choosing which assumed vector positions x'"'"'_ij are to be vector positions x_ij.

21. A method of choosing the vector positions, x_ij, at which measurements are to be made in a pattern recognition system comprising the steps of:

assuming a set of vector positions, x'"'"'_ij,

making energy intensity measurements at each assumed vector position, x'"'"'_ij, for each one of a training set of items;

performing a partial F-value test using the results of said measurements for each assumed vector positions, x'"'"'_ij ; and

,

testing the results of each partial F-value test to determine if the result is geater than F₁,N-k-1;

γ

where γ

is the gamma distribution point of Fisher'"'"'s F distribution with 1 and N-k-1 degrees of freedom, N is equal to the number of samples used in the training set and k is the total number of vector positions x_ij to be determined; and

,

choosing the assumed vector positions, x'"'"'_ij, to be vector positions, x_ij, at which measurements are to be made if the result of their related partial F-value test is greater than F₁,N-k-1;

γ

.

22. A method of choosing the vector positions, x_ij, at which measurements are to be made in a pattern recognition system as claimed in claim 21 including the further step of determining if each assumed variable, x'"'"'_ij, that has a partial F-value test result greater than F₁,N-k-1;

γ

, has a large probability of having a null value and rejecting the assumed variables, x'"'"'_ij, that have a large probability of having a null value prior to choosing which assumed vector positions x'"'"'_ij are to be vector positions x_ij.