Apparatus for the identification of free-lying cells

• US 6,137,899 A
• Filed: 07/22/1998
• Issued: 10/24/2000
• Est. Priority Date: 09/20/1994
• Status: Expired due to Term

• Current Assignee: Tripath Imaging Incorporated (Becton, Dickinson & Co)
• Original Assignee: Tripath Imaging Incorporated (Becton, Dickinson & Co)
• Inventors: Wilhelm, Paul S., Meyer, Michael G., Oh, Seho, Lee, Shih-Jong J., Kuan, Chih-Chau L., Bannister, Wendy R.
• Primary Examiner(s): Tran, Phuoc
• Application Number:
  US09/120,860
• Time in Patent Office: 825 Days
• Field of Search: 382/133, 382/134, 382/226, 382/228
• US Class Current: 382/133
• CPC Class Codes: G01N 15/1433 using image recognition
• CPC Class Codes: G01N 2015/1497 Particle shape
• CPC Class Codes: G06V 20/69 Microscopic objects, e.g. biological cells or cellular parts

Abstract

A free-lying cell classifier. An automated microscope system comprising a computer and high speed processing field of view processors identifies free-lying cells. An image of a biological specimen is obtained and the image is segmented to create a set of binary masks. The binary masks are used by a feature calculator to compute the features that characterize objects of interest including free-lying cells, artifacts and other biological objects. The objects are classified to identify their type, their normality or abnormality or their identification as an artifact. The results are summarized and reported. A stain evaluation of the slide is performed as well as a typicality evaluation. The robustness of the measurement is also quantified as a classification confidence value. The free-lying cell evaluation is used by an automated cytology system to classify a biological specimen slide.

Claims

1. A feature classifier apparatus for performing a plurality of stages of feature extraction and object classification on cells in a biological specimen, the apparatus comprising:

(a) means for acquiring at least one image of a biological specimen;

(b) an initial stage classifier means for determining whether objects in the at least one image are object types of interest and other objects;

(c) a sequence of object classifiers wherein each object classifier has an object type of interest input, an object type of interest output and an other object type output, and wherein the object type of interest output is connected to the object type of interest input of a next classifier in the sequence; and

(d) a diagnostic classifier means for determining whether objects of interest from a final classifier in the sequence of classifiers are low grade squamous intraepithelial lesions, potential high grade squamous intraepithelial lesions, cancerous lesions or normal artifacts.

2. The apparatus of claim 1 wherein the diagnostic classifier means further comprises a ploidy classifier, wherein the ploidy classifier further comprises:

(a) means for computing a first probability that an object is abnormal;

(b) means for computing whether the object is classified as aneuploid;

(c) means for computing a second probability that the object is aneuploid; and

(d) means for combining the first probability and the second probability to provide a final confidence.

3. The apparatus of claim 1 wherein object types of interest comprise normal cells, abnormal cells and artifacts.

4. The apparatus of claim 1 further comprising

(a) a feature combination classifier for classifying objects as normal or abnormal;

(b) a means for computing a first probability of abnormal objects being abnormal;

(c) a means for combining a second set of features to determine whether an object is classified as normal or abnormal;

(d) a means for computing a second probability of the object being abnormal; and

(e) a means for combining the first probability and the second probability to produce a final confidence factor.

5. The apparatus of claim 1 wherein the object types of interest comprise reference intermediate cells.

6. The apparatus of claim 1 wherein the object types of interest comprise cancerous and precancerous cells.

7. The apparatus of claim 1 wherein the biological specimen is a specimen prepared by the Papanicolaou method.

8. The apparatus of claim 1 wherein the biological specimen is a gynecological specimen.

9. The apparatus of claim 1 further comprising a means for computing an atypicality index.

10. The apparatus of claim 1 wherein at least one of the classifiers in the sequence of object classifiers comprises a box filter.

11. The apparatus of claim 1 wherein at least one of the classifiers in the sequence of object classifiers comprises a decision tree classifier.

12. The apparatus of claim 1 wherein at least one of the classifiers in the sequence of object classifiers comprises a binary decision tree classifier.

13. The apparatus of claim 1 wherein at least one of the classifiers in the sequence of object classifiers comprises a fuzzy classifier.

14. The apparatus of claim 1 wherein at least one of the classifiers in the sequence of object classifiers further comprises means for measuring confidence.

15. The apparatus of claim 1 further including a plurality of computer processors wherein the plurality of computer processors perform multilayered processing.

16. A feature classifier apparatus for performing a plurality of stages of feature extraction and object classification on cells in a biological specimen, the apparatus comprising:

(a) means for acquiring at least one image of a biological specimen;

(b) an initial stage classifier means for determining whether objects in the at least one image are object types of interest and other objects;

(c) a sequence of object classifiers wherein each object classifier has an object type of interest input, an object type of interest output and an other object type output, and wherein the object type of interest output is connected to the object type of interest input of a next classifier in the sequences;

(d) an initial box filter means for determining whether objects are normal, potentially abnormal or artifacts;

(e) a stage 1 classifier means for processing the normal and potentially abnormal objects into a potentially abnormal, artifact or normal object;

(f) a stage 2 classifier means for determining whether the potentially abnormal objects from the stage 1 classifier are potentially abnormal, artifact or normal;

(g) a stage 3 classifier for determining whether the potentially abnormal objects from the stage 2 classifier are potentially abnormal or are normal and artifact objects; and

(h) a stage 4 classifier for determining whether the potential abnormal objects; and

from the stage 3 classifier are potentially abnormal or normal artifacts.

17. The apparatus of claim 16 wherein object types of interest comprise normal cells, abnormal cells and artifacts.

18. The apparatus of claim 17 wherein the normal cells comprise reference intermediate cells.

19. The apparatus of claim 17 wherein the abnormal cells comprise cancerous and precancerous cells.

20. The apparatus of claim 16 wherein the biological specimen is a specimen prepared by the Papanicolaou method.

21. The apparatus of claim 16 wherein the biological specimen is a gynecological specimen.

22. The apparatus of claim 16 further comprising a means for computing an atypicality index.

23. The apparatus of claim 16 wherein the initial box filter means further comprises a filter selected from the group consisting of a dark object filter, an unfocused object filter, a polymorphonuclear leukocytes filter, a graphite filter, and a cytoplasm filter.

24. The apparatus of claim 16 wherein at least one of the classifiers in the sequence of object classifiers comprises a box filter.

25. The apparatus of claim 16 wherein at least one of the classifiers in the sequence of object classifiers comprises a decision tree classifier.

26. The apparatus of claim 16 wherein at least one of the classifiers in the sequence of object classifiers comprises a binary decision tree classifier.

27. The apparatus of claim 16 wherein at least one of the classifiers in the sequence of object classifiers comprises a fuzzy classifier.

28. The apparatus of claim 16 wherein at least one of the classifiers in the sequence of object classifiers further comprises means for measuring confidence.

29. The apparatus of claim 16 wherein the stage 4 classifier comprises:

(a) a feature combination classifier for classifying objects as normal or abnormal;

(b) a means for computing a first probability of abnormal objects being abnormal;

(c) a means for combining a second set of features to determine whether an object is classified as normal or abnormal;

(d) a means for computing a second probability of the object being abnormal; and

(e) a means for combining the first probability and the second probability to produce a final confidence factor.

30. The apparatus of claim 16 further including a plurality of computer processors wherein the plurality of computer processors perform multilayered processing.

31. A feature classification apparatus for performing a plurality of stages of feature extraction and object classification on cells in a biological specimen, the apparatus comprising:

(a) an initial box filter means for determining whether objects are normal and potentially abnormal or artifacts;

(b) a stage 1 classifier means for processing the normal and potentially abnormal objects into a potentially abnormal, artifact or normal object;

(c) a stage 2 classifier means for determining whether the potentially abnormal objects from the stage 1 classifier are potentially abnormal, artifact or normal;

(d) a stage 3 classifier for determining whether the potentially abnormal objects from the stage 2 classifier are potentially abnormal or are normal and artifact objects; and

(e) a stage 4 classifier for determining whether the potential abnormal objects from the stage 3 classifier are potentially abnormal or are normal artifacts.

32. The apparatus of claim 31 wherein the initial box filter means further comprises a filter selected from the group consisting of a dark object filter, an unfocused object filter, a polymorphonuclear leukocytes filter, a graphite filter, and a cytoplasm filter.

33. The apparatus of claim 31 wherein the stage 4 classifier comprises:

(a) a feature combination classifier for classifying objects as normal or abnormal;

(b) a means for computing a first probability of abnormal objects being abnormal;

(c) a means for combining a second set of features to determine whether an object is classified as normal or abnormal;

(d) a means for computing a second probability of the object being abnormal; and

(e) a means for combining the first probability and the second probability to produce a final confidence factor.

34. The apparatus of claim 31 wherein the normal objects comprise reference intermediate cells.

35. The apparatus of claim 31 wherein the abnormal objects comprise cancerous and precancerous cells.

36. The apparatus of claim 31 wherein the biological specimen is a specimen prepared by the Papanicolaou method.

37. The apparatus of claim 31 wherein the biological specimen is a gynecological specimen.

38. The apparatus of claim 31 further comprising a means for computing an atypicality index.

39. The apparatus of claim 31 wherein at least one of the classifiers comprises a box filter.

40. The apparatus of claim 31 wherein at least one of the classifiers comprises a decision tree classifier.

41. The apparatus of claim 31 wherein at least one of the classifiers comprises a binary decision tree classifier.

42. The apparatus of claim 31 wherein at least one of the classifiers comprises a fuzzy classifier.

43. The apparatus of claim 31 wherein at least one of the classifiers comprises means for measuring confidence.

44. The apparatus of claim 31 further including a plurality of computer processors wherein the plurality of computer processors perform multilayered processing.

45. A feature classifier apparatus for performing a plurality of stages of feature extraction and object classification on cells in a biological specimen comprising:

(a) means for acquiring at least one image of a biological specimen;

(b) an initial stage classifier means for determining whether objects in the at least one image are object types of interest and other objects;

(c) a sequence of object classifiers wherein each object classifier has an object type of interest input, an object type of interest output and an other object type output, and wherein the object type of interest output is connected to the object type of interest input of a next classifier in the sequence;

(d) a means for computing an atypicality index; and

(e) a diagnostic classifier means for determining whether objects of interest in an output of a stage 3 classifier are low grade squamous intraepithelial lesions, potential high grade squamous intraepithelial lesions, cancerous lesions or normal artifacts.