Method and apparatus for automated image analysis of biological specimens
First Claim
1. An apparatus for automatic image analysis of a slide having a biological specimen, comprising:
 a computer comprising;
at least one system processor;
a monitor in operable communication with the computer; and
an input device in communication with the computer;
an optical system in operable communication with the computer, comprising;
a movable stage;
an automated loading and unloading member for loading and unloading of a slide;
an identification member;
an optical sensing array in optical communication with the stage configured to acquire an image at each location in a scan area;
an image processor in electrical communication with the sensing array and operable to process each image to detect candidate objects of interest in the image through an automated process;
a storage member for storing the location of a candidate object of interest; and
a storage device for storing each image.
6 Assignments
Litigations
0 Petitions
Accused Products
Abstract
A method and apparatus for automated cell analysis of biological specimens automatically scans at a low magnification to acquire images (288) which are analyzed to determine candidate cell objects of interest. The low magnification images are converted from a first color space to a second color space (290). The color space converted image is then low pass filtered (292) and compared to a threshold (294) to remove artifacts and background objects from the candidate object of interest pixels of the color converted image. The candidate object of interest pixels are morphologically processed (296) to group candidate object of interest pixels together into groups which are compared to blob parameters (298) to identify candidate objects of interest which correspond to cells or other structures relevant to medical diagnosis of the biological specimen. The location coordinates of the objects of interest are stored and additional images of the candidate cell objects are acquired at high magnification. The high magnification images are analyzed in the same manner as the low magnification images to confirm the candidate objects of interest which are objects of interest. A high magnification image of each confirmed object of interest is stored for later review and evaluation by a pathologist.
124 Citations
31 Claims

1. An apparatus for automatic image analysis of a slide having a biological specimen, comprising:

a computer comprising;
at least one system processor;
a monitor in operable communication with the computer; and
an input device in communication with the computer;
an optical system in operable communication with the computer, comprising;
a movable stage;
an automated loading and unloading member for loading and unloading of a slide;
an identification member;
an optical sensing array in optical communication with the stage configured to acquire an image at each location in a scan area;
an image processor in electrical communication with the sensing array and operable to process each image to detect candidate objects of interest in the image through an automated process;
a storage member for storing the location of a candidate object of interest; and
a storage device for storing each image.  View Dependent Claims (2, 3, 4, 5, 6, 7, 8)


9. A method for automatic image analysis of a slide having a biological specimen, comprising:

positioning the slide having a biological specimen on a stage which is optically coupled to an optical sensing array;
acquiring an image of the biological specimen;
processing the image;
identifying a candidate object of interest by means of a computer processor;
storing the coordinates of the candidate object of interest;
acquiring a higher magnification image at the coordinates of the object of interest;
processing the higher magnification image; and
storing the higher magnification image, wherein the step of identifying the candidate object of interest is by an automated process.  View Dependent Claims (10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31)
computing a mean value of said candidate object of interest pixels; specifying a threshold factor;
computing a standard deviation for the candidate object of interest pixels; and
setting the threshold to the sum of the mean value and the product of the threshold factor and the standard deviation prior to comparing the candidate object of interest pixels to the threshold. 

20. The method of claim 16, further comprising:

grouping said morphologically processed candidate object of interest pixels into regions of connected candidate object of interest pixels to identify objects of interest;
comparing said objects of interest to blob analysis parameters; and
storing location coordinates of the candidate objects of interest having an area corresponding to the blob analysis parameters.


21. The method of claim 20, wherein the method is performed on images acquired at a low magnification and the method further comprises:

adjusting an optical system viewing the slide from which the objects of interest were identified to a higher magnification;
acquiring a higher magnification image of the slide at the corresponding location coordinates for each candidate object of interest;
transforming pixels of the higher magnification image in the fist color space to a second color space to differentiation higher magnification candidate objects of interest pixels from background pixels; and
identifying higher magnification objects of interest form the candidate object of interest pixels in the second color space.


22. The method of claim 21, further comprising morphologically processing the higher magnification candidate object of interest pixels to identify artifact pixels and identifying the higher magnification objects of interest form the remaining higher magnification candidate object of interest pixels not identified as artifact pixels.

23. The method of claim 22, further comprising filtering said higher magnification candidate object of interest pixels with a low pass filter prior to morphologically processing the low pass filtered higher magnification candidate object of interest pixels.

24. The method of claim 23, further comprising, comparing said low pass filtered higher magnification candidate object of interest pixels to a threshold prior to morphologically processing the higher magnification candidate object of interest pixels which have values greater than or equal to the threshold value.

25. The method of claim 24, further comprising:

computing a mean value of said higher magnification candidate object of interest pixels;
specifying a threshold factor;
computing a standard deviation for the higher magnification candidate object of interest pixels; and
setting the threshold to the sum of the mean value and the product of the threshold factor and the standard deviation prior to comparing the higher magnification candidate object of interest pixels to the threshold.


26. The method of claim 14, further comprising:

grouping said low pass filtered higher magnification candidate object of interest pixels into regions of connected higher magnification candidate object of interest pixels to identify higher magnification object of interest;
comparing said higher magnification objects of interest to blob analysis parameters; and
storing the location coordinates of the higher magnification objects of interest corresponding to the blob analysis parameters.


27. The method of claim 21, wherein the optical system is initially focused prior to performing the low magnification processing.

28. The method of claim 27, wherein the initial focusing of the optical system further comprises:

a) positioning the optical system at an initial Z stage position;
b) acquiring at low magnification an image of a slide having a stained biological specimen thereon and calculating a pixel variance about a pixel mean for the acquired image;
c) incrementing the position of the Z stage;
d) repeating steps (b) and (c) for a fixed number of course iterations to form a first set of variance data;
e) performing a least squares fit of the first set of variance data to a first function;
f) position the Z stage at a position near the peak of the first iteration;
g) repeating steps (b) and (c) for a fixed number of fine iterations to form a second set of variance data;
h) performing a least squared fit of the second set of variance data to a second function;
i) selecting the peak value of the least squares fit curve as an estimate of the best focal position; and
j) performing the above steps for an array of stage positions to form an array of focal positions and performing a least squares fit of the array of focal positions to yield a least squares for focal plane.


29. The method of claim 27, wherein the initial focusing of the optical system further comprises the steps of:

a) positioning the optical system at an initial Z stage position;
b) acquiring an image and calculating a pixel variance about a pixel mean for the acquired image;
c) incrementing the position of the Z stage;
d) repeating steps (b) and (c) or a fixed number of iterations;
e) performing a least squares fit of the variance data to a known function; and
f) selecting the peak value of the least squares fit curve as an estimate of the best focal position.


30. The method of claim 21, wherein adjusting the optical system further comprises the steps of:

a) positioning the optical system at an initial Z stage position;
b) acquiring an image and selecting a center pixel of a candidate object of interest;
c) defining a region of interest centered about the selected center pixel;
d) performing a fast fourier transform of said region of interest to identify frequency components for the region of interest and complex magnitudes for the frequency components;
e) computing a power value by summing the square of the complex magnitudes for the frequency components that are within the rage of frequencies of 25% to 75% of a maximum frequency component of the fast fourier transform of the region of interest;
f) incrementing the position of the Z stage;
g) repeating steps (b) to (e) for a fixed number of iterations; and
h) selecting the Z stage position corresponding to the largest power value as the best focal position.


31. The method of claim 21, wherein adjusting the optical system further comprises the steps of:

a) positioning the optical system at an initial Z stage position;
b) acquiring an image and selecting a center pixel of a candidate object of interest;
c) defining a region of interest centered about the selected center pixel;
d) applying a Hanning window function to the region of interest;
e) performing a fast fourier transform of said region of interest following the application of the Hanning window function to identify frequency components of the region of interest and complex magnitudes for the frequency components;
f) computing a power value by summing the square of the complex magnitudes for the frequency components for the fast fourier transform of the region of interest;
g) incrementing the position of the Z stage;
h) repeating steps (b) to (e) for a fixed number of iterations; and
i) selecting the Z stage position corresponding to the largest power value as the best focal position.

1 Specification