1. An automated method for biological sample analysis, comprising:
automatically analyzing luminance parameters from a digital image of a biological tissue sample to which a marker dye has been applied to determine one or more areas of interest in the biological tissue sample within the digital image;
automatically adjusting luminance parameters within the one or more determined areas of interest within the digital image to create one or more adjusted areas of interest;
automatically analyzing morphological parameters from individual biological components within the one or more adjusted areas of interest within the digital image; and
automatically formulating a medical diagnosis using the analyzed morphological parameters within the one or more adjusted areas of interest within the digital image.
2. The method of claim 1 further comprising a computer readable medium have stored therein instructions for causing a processor to execute the steps of the method.
3. The method of claim 1 wherein the marker dye includes immunohistochemical (IHC) staining.
4. The method of claim 1 wherein the biological tissue sample includes a plurality of human cells.
5. The method of claim 4 wherein the plurality of human cells includes one or more human cancer cells.
6. The method of claim 1 wherein the step of automatically analyzing luminance parameters includes analyzing a plurality of luminosities Y at a plurality of pixels within the digital image with:
Y=XG+YR+XB,
wherein X, Y and Z are pre-determined constants R, G and B are Red, Green and Blue color component values of a selected pixel within the digital image and X, Y and Z are predetermined constant values.
7. The method of claim 1 wherein the step of automatically analyzing luminance parameters includes determining an area of interest by excluding pixels which include biological tissue samples from background pixels in the digital image.
8. The method of claim 1 wherein the step of automatically adjusting luminance parameters adjusting luminance parameters within the one or more determined areas of interest includes:
modifying a contrast of the digital image based on statistics collected from the digital image to created a contrast modified digital image;
thresholding the contrast modified digital image to obtain a plurality of thresholded pixels including one or more areas of interest; and
correcting color components of the plurality of thresholded pixels within the one or more areas of interest in the contrast modified digital image.
9. The method of claim 8 wherein the step of modifying a contrast includes calculating for a plurality of pixels in the digital image:
R\u2032=(R*(M+D)K)+(X\u2212M),
G\u2032=(G*(M+D)K)+(X\u2212M),
B\u2032=(B*(M+D)K)+(X\u2212M),
wherein K is a constant, R\u2032, G\u2032 and B\u2032 are modified red (R), green (G) and blue (B) color components of a pixel, M is a mean and D is a standard deviation of a luminosity histogram calculated for the plurality of pixels in the digital image and X is a value of a pre-determined constant.
10. The method of claim 8 wherein the step of thresholding the contrast modified digital image includes:
calculating a luminosity histogram on the contrast modified digital image, wherein the luminosity histogram includes two or more peaks for grayscale values for a plurality of pixels in the digital image; and
selecting a plurality of pixels on the contrast modified digital image with a grayscale value less than or equal to a maximum grayscale value for a first peak in the luminosity histogram.
11. The method of claim 8 wherein the step of correcting color components includes:
calculating a correction factor Cf,
wherein
Cf=(mean of a first color plane)(mean of a second color plane) in the original digital image; and
correcting color components of the plurality of selected pixels within the one or more items of interest in the contrast modified digital image by multiplying the plurality of selected pixels by the correction factor Cf.
12. The method of claim 11 wherein the first color plane includes a red color plane and the second color plane include a blue color plane.
13. The method of claim 1 wherein step of automatically analyzing morphological parameters includes analyzing membranous rings around cell nuclei.
14. The method of claim 1 wherein the step of automatically formulating a medical diagnosis includes:
broadly classifying into two or more groups based on a presence of pre-determined pixels using the analyzed morphological parameters within the one or more adjusted areas of interest within the digital image;
detecting one or more predetermined analyzed morphological parameters within the one or more adjusted areas of interest within the digital image; and
preparing a final grading based on a completeness of the one or more predetermined analyzed morphological parameters.
15. The method of claim 14 wherein the broadly classifying step includes broadly classifying for human epidermal growth factor receptor HER-2neu grading for a plurality of human tissue cells stained with HematoxylinEosin (HE) staining and grouping 0+ and 1+ HER-2neu graded cells into a first group and 2+ and 3+ HER-2neu graded cells into a second group based on a ratio of brown pixels versus blue pixels.
16. The method of claim 14 wherein the detecting step includes:
detecting a clear and complete cell membrane ring around a cell nucleus; and
detecting an intensity of a membranous pattern for the cell membrane ring.
17. The method of claim 1 wherein the step of automatically formulating a medical diagnosis includes formulating a medical diagnosis based on human epidermal growth factor receptor HER-2neu grading for a plurality of human tissue cells stained with immunohistochemical (IHC) staining.
18. An automated method for biological sample analysis, comprising:
automatically segmenting morphological components from a biological tissue sample to which a marker dye has been applied within a digital image into one or more areas of interest;
automatically adjusting viewable characteristics of the segmented morphological components using one or more digital image processing techniques to create one or more adjusted areas of interest within the digital image; and
automatically computing a medical diagnosis grade based on the segmented morphological components within the one or more adjusted areas of interest within the digital image.
19. The method of claim 17 further comprising a computer readable medium having stored therein instructions for causing a processor to execute the steps of the method.
20. The method of claim 18 wherein the step of automatically segmenting morphological components includes segmenting cell nuclei and cell membranes from cytoplasm, fibrin and other components in the biological tissue sample within the digital image.
21. The method of claim 18 wherein the step of automatically adjusting viewable characteristics includes:
modifying a contrast of the digital image based on statistics collected from the digital image to created a contrast modified digital image;
thresholding the contrast modified digital image to obtain a plurality of thresholded pixels in the one or more areas of interest; and
correcting color components of the plurality of thresholded pixels within the one or more areas of interest in the contrast modified digital image to create one or more adjusted areas of interest within the digital image.
22. The method of claim 18 wherein the step of automatically computing a medical diagnosis grade includes computing a human epidermal growth factor receptor HER-2neu grade based on continuity of cell membranous rings around cell nuclei within the one or more adjusted areas of interest within the digital image.
23. An automated method for biological sample analysis, comprising:
automatically analyzing luminance parameters from a digital image of a biological tissue sample to which a marker dye has been applied to determine one or more areas of interest in the biological tissue sample within the digital image;
automatically adjusting luminance parameters within the one or more determined areas of interest within the digital image to create one or more adjusted areas of interest;
automatically identifying a plurality of epithelial areas in the one or more adjusted areas of interest within the digital image for cell classification using cell membrane analysis;
automatically identifying a plurality of cell nuclei with the plurality epithelial areas in the one or more adjusted areas of interest within the digital image;
automatically identifying a plurality of cell membranes in the one or more adjusted areas of interest within the digital image;
automatically classifying the plurality of identified cell nuclei with a pre-determined classification scheme; and
automatically computing a medical diagnosis grade based on the classified cell nuclei.
24. The method of 23 further comprising a computer readable medium having stored therein instructions for causing a processor to execute the steps of the method.
25. The method of claim 23 wherein the step of automatically computing a medical diagnosis grade includes computing a human epidermal growth factor receptor HER-2neu grade based on continuity of cell membranous rings around cell nuclei within the one or more adjusted areas of interest within the digital image.
26. The method of claim 25 wherein the HER-2neu grade includes 0+, 1+, 2+ and 3+, HER-2neu grading for a plurality of human tissue cells stained with IHC and counter stained with haematoxylin.
27. A biological sample analysis system, comprising in combination:
an automated analysis means for analyzing luminance parameters from a digital image of a biological tissue sample to which a marker dye has been applied to determine one or more areas of interest in the biological tissue sample within the digital image, for adjusting luminance parameters within the one or more determined areas of interest within the digital image to create one or more adjusted areas of interest, and for analyzing morphological parameters from individual biological components within the one or more adjusted areas of interest within the digital image; and
an automated medical diagnosis means for formulating a medical diagnosis using the analyzed morphological parameters within the one or more adjusted areas of interest within the digital image adjusted by the automated analysis means.
28. The biological sample analysis system of claim 27 wherein the automated medical diagnosis means formulates medical diagnosis based on human epidermal growth factor receptor HER-2neu grading for a plurality of human tissue cells stained with immunohistochemical (IHC) staining and counterstained with haematoxylin using analyzed cell morphological parameters.
29. A biological sample analysis system, comprising in combination:
a module for automatically analyzing luminance parameters from a digital image of a biological tissue sample to which a marker dye has been applied to determine one or more areas of interest in the biological tissue sample within the digital image and for automatically adjusting luminance parameters within the one or more determined areas of interest within the digital image to create one or more adjusted areas of interest;
a module for automatically identifying a plurality of epithelial areas in the one or more adjusted areas of interest within the digital image for cell classification using cell membrane analysis, for automatically identifying a plurality of cell nuclei with the plurality epithelial areas in the one or more adjusted areas of interest within the digital image, for automatically identifying a plurality of cell membranes in the one or more adjusted areas of interest within the digital image, for automatically classifying the plurality of identified cell nuclei with a pre-determined classification scheme; and
a module for automatically computing a medical diagnosis grade based on the classified cell nuclei.
30. An automated method for biological sample analysis, comprising:
automatically analyzing luminance parameters from a digital image of a biological tissue sample to which a marker dye has been applied to determine one or more areas of interest in the biological tissue sample within the digital image;
automatically adjusting luminance parameters within the one or more determined areas of interest within the digital image to create one or more adjusted areas of interest;
automatically analyzing morphological parameters from individual biological components within the one or more adjusted areas of interest within the digital image; an
automatically formulating a medical diagnosis using the analyzed morphological parameters within the one or more adjusted areas of interest within the digital image and one or more diagnostic knowledge records from a knowledge database; and
automatically saving the formulated medical diagnosis in the knowledge database to create additional diagnostic knowledge.
31. The method of claim 1 further comprising a computer readable medium have stored therein instructions for causing a processor to execute the steps of the method.
32. An automated method for biological sample analysis, comprising:
automatically formulating a medical diagnosis using analyzed morphological parameters within one or more adjusted areas of interest within a digital image of a biological tissue sample to which a chemical compound has been applied;
providing the medical diagnosis to a medical professional for verification of the automatically formulated medical diagnosis; and
automatically saving the automatically formulated medical diagnosis in the knowledge database to create additional diagnostic knowledge.
33. The method of claim 32 further comprising a computer readable medium have stored therein instructions for causing a processor to execute the steps of the method.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A mast structure for a lift truck comprising:
a mast comprising a pair of parallel uprights;
a carriage for supporting a load mounted on the uprights for movement along a height of the mast;
an elongated guide member secured to the mast between the uprights; and
a guided member mounted on the carriage and engageable with the guide member as the carriage moves along the mast to resist forces acting on the carriage in a widthwise direction of a lift truck and moments acting on the carriage about an axis extending in a fore and aft direction of the lift truck when the mast is mounted on the lift truck.
2. A mast structure as claimed in claim 1 wherein the guided member comprises a roller mounted on the carriage and rollable along the guide member.
3. A mast structure as claimed in claim 2 including two rollers mounted on the carriage and spaced from each other in a lengthwise direction of the guide member and rollable along the guide member.
4. A mast structure as claimed in claim 3 wherein the guide member includes a pair of opposing flanges, and the two rollers are disposed between the opposing flanges.
5. A mast structure as claimed in claim 3 wherein the guide member passes between the two rollers.
6. A mast structure as claimed in claim 1 wherein the guided member can slide along the guide member as the carriage moves along the mast.
7. A mast structure as claimed in claim 6 wherein the guided member and the guide member interfit.
8. A mast structure as claimed in claim 6 wherein the guide member extends around the guide member.
9. A lift truck comprising:
a truck body equipped with a plurality of wheels; and
a mast structure as claimed in claim 1 mounted on the truck body.
10-15. (canceled)
16. A lift truck comprising:
a truck body having a plurality of wheels; and
a mast supported by the truck body and comprising a pair of first uprights disposed parallel to each other, a pair of second uprights disposed parallel to each other on opposite widthwise sides of the first uprights, and a guide roller supported by one of the uprights for rolling contact with another of the uprights and having a rotational axis extending in a fore-and-aft direction of the lift truck.
17. A lift truck as claimed in claim 16 wherein the guide roller is disposed at an upper end of one of the second uprights opposing an adjoining one of the first uprights.
18. A lift truck as claimed in claim 16 wherein the guide roller is disposed at a lower end of one of the first uprights opposing an adjoining one of the second uprights.
19. A lift truck as claimed in claim 16 including a first guide roller disposed at an upper end of one of the second uprights opposing an adjoining one of the first uprights and a second guide roller disposed at a lower end of one of the first uprights opposing an adjoining one of the second uprights.
20. A mast structure as claimed in claim 3 wherein each roller has an axis of rotation extending in the fore and aft direction of the lift truck when the mast is mounted on the lift truck.
21. A mast structure as claimed in claim 1 wherein the mast comprises a first pair of parallel uprights engaged with the carriage and a second pair of parallel uprights extending parallel to the first uprights and guiding the first pair of uprights for vertical movement with respect to the second pair of uprights, and the guide member is vertically movable together with the first pair of parallel uprights with respect to the second pair of uprights.
22. A mast structure as claimed in claim 1 wherein the mast includes a pair of first uprights disposed parallel to each other, a pair of second uprights disposed parallel to each other on opposite widthwise sides of the first uprights, and a guide roller supported by one of the uprights for rolling contact with another of the uprights and having a rotational axis extending in a fore-and-aft direction of the lift truck when the mast is mounted on the lift truck.
23. A mast structure as claimed in claim 22 including a first guide roller disposed at an upper end of one of the second uprights opposing an adjoining one of the first uprights and a second guide roller disposed at a lower end of one of the first uprights opposing an adjoining one of the second uprights.