Patent Publication Number: US-8116550-B2

Title: Method and system for locating and focusing on fiducial marks on specimen slides

Description:
RELATED APPLICATION DATA 
     The present application claims the benefit under 35 U.S.C. §119 to U.S. provisional patent application Ser. No. 60/871,131, filed Dec. 20, 2006. The foregoing application is hereby incorporated by reference into the present application in its entirety. 
    
    
     GOVERNMENT RIGHTS 
     This invention was made with U.S. Government support under NIH Grant No. RR018046. The U.S. Government may have certain rights in this invention. 
    
    
     FIELD OF THE INVENTION 
     The invention relates to imaging and analysis of biological specimens, and more particularly, to locating and focusing on fiducial marks on specimen slides. 
     BACKGROUND 
     Medical professionals and cytotechnologists often prepare biological specimens on a specimen carrier, such as a slide, and review specimens to analyze whether a patient has or may have a particular medical condition or disease. For example, it is known to examine a cytological specimen in order to detect malignant or pre-malignant cells as part of a Papanicolaou (Pap) smear test and other cancer detection tests. To facilitate this review process, automated systems focus the technician&#39;s attention on the most pertinent cells or groups of cells, while discarding less relevant cells from further review. One known automated imaging system that has been effectively used in the past is the ThinPrep Imaging System, available from Cytyc Corporation, 250 Campus Drive, Marlborough, Mass. 01752. 
       FIG. 1  generally illustrates one known biological screening system  10  that is configured for presenting a biological specimen  12  located on a microscope slide  14  (as shown in  FIG. 2 ) to a technician, such as a cytotechnologist, who can then review objects of interest (OOIs) located in the biological specimen  12 . The OOIs are arranged in a number of fields of interest (FOIs) that cover portions of the slide  14 , so that the cytotechnologist&#39;s attention can be subsequently focused on OOIs within the FOIs, rather than slide regions that are not pertinent. The system  10  can be used for the presentation of cytological cervical or vaginal cellular material, such as that typically found on a Pap smear slide. In this case, the OOIs take the form of individual cells and cell clusters that are reviewed to check for the possible presence of an abnormal condition, such as malignancy or pre-malignancy. 
     The biological specimen  12  will typically be placed on the slide  14  as a thin cytological layer. A cover slip (not shown in  FIG. 1 ) is preferably adhered to the specimen  12  to fix the specimen  12  in position on the slide  14 . The specimen  12  may be stained with any suitable stain, such as a Papanicolaou stain. 
     An imaging station  18  is configured to image the slide  14 , which is typically contained within a cassette (not shown in  FIG. 1 ) along with other slides. During the imaging process, slides are removed from the respective cassettes, imaged, and returned to the cassettes in a serial fashion. 
     One known imaging station  18  includes a camera  24 , a microscope  26 , and a motorized stage  28 . The camera  24  captures magnified images of the slide  14  through the microscope  26 . The camera  24  may be any one of a variety of conventional cameras, such as cameras that can produce a digital output of sufficient resolution to allow processing of the captured images. A suitable resolution may be 640×480 pixels. Each pixel can be converted into an eight-bit value (0 to 255) depending on its optical transmittance. A value of “00000000” or “0” is the assigned value for least amount of light passing through the pixel, and a value of “11111111” or “255” is the assigned value for a greatest amount of light passing through the pixel. Thus, a “0” value indicates a dark value, e.g., a pixel of a fiducial mark, and a “255” value indicates a light value, e.g., an empty pixel. 
     The slide  14  is mounted on the motorized stage  28 , which scans the slide  14  relative to the viewing region of the microscope  26 , while the camera  24  captures images over various regions of the biological specimen  12 . The motorized stage  28  tracks the x−y coordinates of the images as they are captured by the camera  24 . Encoders (not shown) can be coupled to the respective motors of the motorized stage  28  in order to track the net distance traveled in the x- and y-directions during imaging. 
     Referring to  FIG. 2 , x−y coordinates tracked by the stage  28  are measured relative to fiducial marks  16  affixed to the slide  14 . A fiducial mark  16  may be a rectangular patch of paint; in this case, one corner of the mark may be considered to be the marks&#39;s location. These fiducial marks  16  are also used by the reviewing station  22  to ensure that the x−y coordinates of the slide  14  during the review process can be correlated to the x−y coordinates of the slide  14  obtained during the imaging process. 
     More particularly, each reviewing station  20  includes a microscope  38  and a motorized stage  40 . The slide  14  (after image processing) is mounted on the motorized stage  40 , which moves the slide  14  relative to the viewing region of the microscope  38  based on the routing plan and a transformation of the x−y coordinates of the FOIs obtained from memory  36 . These x−y coordinates, which were acquired relative to the x−y coordinate system of the imaging station  18 , are transformed into the x−y coordinate system of the reviewing station  20  using the fiducial marks  16  affixed to the slide  14  (shown in  FIG. 1 ). In this manner, the x−y coordinates of the slide  14  during the reviewing process are correlated to the x−y coordinates of the slide  14  during the imaging process. The motorized stage  40  then moves according to the transformed x−y coordinates of the FOIs, as dictated by the routing plan. 
     While known fiducial marks and coordinate systems used during imaging and review processes have been used effectively in the past, they can be improved. In particular, it can be difficult to locate fiducial marks in the presence of air bubbles and to focus on fiducial marks in the presence of dust and debris, as shown with reference to  FIGS. 3-8 . 
       FIG. 3  is a top view of a specimen slide  14  having three fiducial marks  16 . A cover slip  50  is placed over the specimen  12 .  FIG. 4  is a top view of the slide  14  shown in  FIG. 3  having dust or debris (generally dust  52 ) on the cover slip  50 .  FIG. 4  also illustrates an air bubble  54  underneath the cover slip  50 .  FIG. 5  is a side view of  FIG. 4 , which further illustrates dust  52  on top of the cover slip  50  and an air bubble  54  between the top of the slide  14  and the bottom of the cover slip  50 . 
     Persons skilled in the art will appreciate that the dimensions shown in  FIGS. 2-5  and other figures may not reflect actual dimensions and may not be to relative scale and are provided for purposes of illustration. 
     Referring to  FIG. 6 , when a cover slip  50  is placed on a slide, one or more air bubbles  54  may be trapped in the mounting medium  60  between the cover slip  50  and the slide  14 . The fiducial mark  16  may appear to be a different shape and its true outline may be difficult to locate when an air bubble  54  or cellular debris overlaps the fiducial mark  16 . 
     In addition, assuming a fiducial mark  16  is located, dust and debris  52  on top of the cover slip  50  may cause focusing errors. Automatic focusing on a specimen is generally done by focusing up and down until the objects in the image are in focus. Referring to  FIG. 7 , dust  52  or other debris (such as a fingerprint) on top of the cover slip  50  may cause an automatic focusing system or algorithm to focus on the dust  52 , thereby resulting in a false plane  72 , rather than locating the correct focal plane  70  corresponding to the sample  12  on the slide (which is coplanar with the fiducial marks  16 ). 
     The dotted line  80  in  FIG. 8  shows focus or sharpness values for a set of images taken of a single microscope field at different focal heights. The field contains a fiducial mark  16  and also contains dust  52 . The “x” axis represents a vertical distance (in microns) from the first or true focus plane  70 . The second, false focus plane  72  is about 110 microns higher than the first focus plane  70 ; the false plane corresponds to the dust  52 , which rests on top of the cover slip  50 , which is about 100 microns thick. The “y” axis represents the logarithm of the Brenner score of the image. The Brenner score is a known method of quantifying image sharpness, and is the sum of the squares of the differences between the gray value of each pixel and its neighbor two pixels to the left, where differences less than a certain threshold are excluded from the sum to reduce the effect of image noise. A higher “y” axis value indicates that the image is in better focus and is sharper or clearer compared to lower “y” axis values. In the illustrated example, an automatic focusing system or algorithm that seeks to maximize the Brenner score would select the false focal plane  72 , because its score is higher than the true focal plane  70 . 
     Consequently, an imaging microscope  26  may focus on the dust  52  in the false focal plane  72  rather than on the fiducial mark  16  at the true focal plane  70 . If the imaging station  18  scans this false focal plane  72  for cells instead of the true focal plane  70 , many images taken of the sample  12  will be out of focus and objects of interest will be missed by the imaging software. 
     Thus, it would be desirable to have methods and systems that can more effectively locate fiducial marks on a specimen slide in the presence of air bubbles or other debris under the cover slip and that can focus on located fiducial marks in the presence of dust and debris on top of the cover slip. 
     SUMMARY OF THE INVENTION 
     In one embodiment, a method of locating a corner of a fiducial mark within an image of a specimen slide is provided, the image having a plurality of pixels, the method including selecting a pixel of the plurality of image pixels, the selected pixel defining an area based on lines extending from the selected pixel, the selected pixel being selected based on a ratio of a number of empty pixels in the defined area and one or more dimensions of the defined area satisfying a threshold, the method further including determining a location of the corner of the fiducial mark using the selected pixel. By way of one non-limiting example, the selected pixel may define the largest area compared to other image pixels that satisfy the threshold. By way of another non-limiting example(s), the boundary lines may be straight, and may extend from the selected pixel to an edge of the image, and an area defined by each pixel is a square or a rectangle. By way of a further, non-limiting example, the ratio can be a ratio of (number of empty pixels in the area defined by the selected pixel) to (one or more dimensions of the area defined by the selected pixel). 
     In another embodiment, a method of locating a corner of a fiducial mark within an image of a specimen slide is provided, the image having a plurality of pixels, the method including identifying a plurality of pixels as candidate pixels, each candidate pixel being identified based on a number of empty pixels in a bounding area defined by lines extending from the candidate pixel relative to one or more dimensions of the defined area; selecting one candidate pixel, wherein lines extending from the selected pixel define the largest bounding area compared to lines extending from other candidate pixels; and determining a location of the corner of the fiducial mark based on the selected candidate pixel. By way of non-limiting example, the bounding area corresponding to the selected pixel may contain the largest number of dark pixels compared to bounding areas defined by other candidate pixels. Again, by way of further non-limiting examples, the lines may be straight, may extend from the selected candidate pixel to an edge of the image, and the bounding area may be defined by each candidate pixel is a box or a rectangle. 
     The each candidate pixel may be identified based on a ratio satisfying a threshold, the ratio comprising a ratio of a number of empty pixels in the bounding area defined by lines extending from the selected candidate pixel to one or more dimensions of the bounding area. For example, each candidate pixel may be selected based on a ratio of the number of empty pixels in the bounding area to one or more dimensions of the bounding area being below a threshold, and the ratio may be a ratio of the number of empty pixels in the bounding area to the perimeter of the bounding area, e.g., a ratio of the number of empty pixels in the bounding area to the semiperimeter of the bounding area. 
     The method may optionally be performed in the presence of an air bubble or debris overlapping the fiducial mark, wherein a pixel may be treated as an empty pixel if a gray value of the pixel is greater than 128. Also, the method may optionally be performed at multiple gray value thresholds, and may further include calculating a focus score based on a difference between a first bounding area and a second bounding area, e.g., wherein the difference is the Euclidean distance between corners of the first and second bounding areas. 
     The method may optionally be performed at multiple gray value thresholds, wherein a first bounding area is identified using a first empty pixel threshold to identify a first location of the corner of the fiducial mark, and a second bounding area is identified using a second empty pixel threshold to identify a second location of the corner of the fiducial mark. The method may optionally further include calculating a focus score based on a distance between the first and second bounding areas, and automatically focusing an image device based on the calculated focus score, e.g., wherein the distance is the Euclidean distance between the corners of the first and second bounding areas. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Referring now to the drawings in which like reference numbers represent corresponding parts throughout and in which: 
         FIG. 1  illustrates a known biological screening system including an imaging station and a reviewing station; 
         FIG. 2  illustrates a known microscope slide carrying a biological specimen and having fiducial marks; 
         FIG. 3  illustrates a known microscope slide carrying a biological specimen and having fiducial marks and a cover slip; 
         FIG. 4  illustrates a known microscope slide having dust or debris on top of a cover slip; 
         FIG. 5  is a side view of  FIG. 4  generally illustrating dust and an air bubble on a specimen slide; 
         FIG. 6  further illustrates an air bubble adjacent to a portion of a fiducial mark; 
         FIG. 7  illustrates different focal planes that are generated as a result of dust or debris as shown in  FIG. 5 ; 
         FIG. 8  is a chart graphically illustrating a false second peak generated by dust or debris; 
         FIG. 9  generally illustrates a corner of a fiducial mark having an edge aligned with an x−y coordinate system and dark and empty pixels enclosed within an area defined by boundary lines extending from a point; 
         FIGS. 10A-B  illustrate that the number of empty pixels contained within a bounding area is proportional to the perimeter of that rectangle; 
         FIGS. 11A-D  further illustrate the proportionality shown in  FIG. 10  based on empty pixels being distributed along an even depth along an edge of the area; 
         FIG. 12  is a flow chart of a method for locating a fiducial mark according to one embodiment; 
         FIG. 13  illustrates a method for location a fiducial mark according to one embodiment; 
         FIG. 14  is a flow chart of a method for locating a fiducial mark according to another embodiment; 
         FIG. 15  generally illustrates a corner of a fiducial mark having a shaped edge with reference to an x−y coordinate system; 
         FIG. 16  illustrates a method of determining a number of empty pixels relative to a given pixel according to one embodiment; 
         FIG. 17  further illustrates a method of determining a number of empty pixels relative to a given pixel according to one embodiment; 
         FIG. 18  further illustrates a method of determining a number of empty pixels relative to a given pixel according to one embodiment; 
         FIG. 19  further illustrates a method of determining a number of empty pixels relative to a given pixel and generating of an empty pixel map according to one embodiment; 
         FIG. 20  is a flow chart of a method of calculating a focus score for an image of a fiducial mark according to one embodiment; 
         FIG. 21  illustrates boundary lines extending from a point and defining different bounding areas as a result of different dark/light threshold values relative to a fiducial mark that is in focus; 
         FIG. 22  illustrates boundary lines extending from a point and defining different bounding areas as a result of different dark/light threshold values relative to a fiducial mark that is less in focus compared to  FIG. 21 ; 
         FIG. 23  illustrates boundary lines extending from a point and defining different bounding areas as a result of different dark/light threshold values relative to a fiducial mark that is less in focus compared to  FIGS. 21 and 22 ; and 
         FIG. 24  is a chart graphically illustrating how embodiments eliminate a false second peak corresponding to dust or debris. 
     
    
    
     DETAILED DESCRIPTION OF ILLUSTRATED EMBODIMENTS 
     Embodiments advantageously locate and focus on fiducial marks in the presence of air bubbles, dust and debris. Embodiments achieve these advantages by fitting a shape, such as a square, a rectangle a box or another shape (generally “bounding box” or “bounding area” or “area”), to an image of the corner of a fiducial mark. Embodiments can be implemented using bounding areas of various shapes. According to one embodiment, a boundary area is defined by two straight lines. The lines can be at various angles. Accordingly, references to a boundary area or box or rectangle are examples of some of the boundary area shapes that can be utilized. 
     The boundaries of the bounding box maximize the area of the bounding box while constraining the number of non-mark or empty pixels within the bounding box. A pixel of one image can be defined as a “dark,” fiducial mark pixel or a “light,” empty pixel based on a gray value cutoff. Focus settings or adjustments can be determined by comparing the bounding areas found using different gray value cutoffs. 
       FIG. 9  illustrates an image of a corner of a fiducial mark  16  that extends beyond the top left corner of an image. The top left corner of the image can be specified as a (0, 0) location, and the bottom right corner of the fiducial mark  16  can be specified as a (x, y) location. Pixels above and to the left of the (x, y) location can be considered to be part of the fiducial mark  16 . Persons skilled in the art will appreciate that other coordinate assignments can be utilized if different portions of the fiducial mark  16  are visible. Thus,  FIG. 9  illustrates one example in which a bottom right corner of the fiducial mark  16  is visible and the coordinate system is based on the bottom right corner of the fiducial mark  16 . 
     In the illustrated example, a horizontal boundary line  94  and a vertical boundary line  95  extend from a point or pixel P to define a bounding box  96 . This specification refers to a bounding box being defined by a point or pixel in the sense that lines  94  and  95  extending from the point or pixel define a bounding box  96 , as well as a bounding box  96  being defined by boundary lines  94  and  95  themselves. In the illustrated embodiment, the boundary lines  94  and  95  are straight and have “x” and “y” dimensions and a perimeter of 2*(x+y). In the illustrated example, the “x” and “y” dimensions are the same, but in other images, they may be different. 
     Due to imprecision in the process of depositing fiducial marks  16  on a slide, edges  92  of the fiducial mark  16  have irregularities on a microscopic scale. For example, as shown in  FIG. 9 , the edges  92  of a fiducial mark  16  may have non-linear or wave-like shapes. As a result of the irregular edge shape, the bounding box  96  may contain some empty pixels  98 ; exclude some fiducial mark pixels  97 , or both. Dark pixels  97  may be pixels of the fiducial mark  16 , and light pixels  98  may be empty pixels. For example, light pixels  98  may be empty pixels that are beyond the edge  92  of the fiducial mark  16  or pixels that represent gaps in the paint of the fiducial mark  16 . For purposes of locating a fiducial mark  16 , a bounding box  96  containing no empty pixels  97  will be too small; a bounding box  96  containing every fiducial mark pixel  97  will be too big. Therefore, the bounding box  96  should be allowed to contain some empty pixels  98 , but not too many empty pixels  98 . 
       FIGS. 10A and 10B  illustrate how two images taken of the same fiducial mark  16  at different slide locations may appear. Point P 1  in  FIG. 10A  and Point P 2  in  FIG. 10B  have been chosen in the two images at the same location relative to the mark  16 . Points P 1  and P 2  define respective bounding rectangles  96  by respective boundary lines  94  and  95  extending from respective points P 1  and P 2  to the edges of the images. 
     As shown in the figures, the empty pixels  98  are indentations in the mark  16  extending from an edge  92  of the fiducial mark  16  to different depths into the fiducial mark  16 . Assuming that the depth of the indentation of empty pixels  98  is a random function with a certain mean and variance, the expected number of empty pixels  98  for the best fit bounding box  96  is equal to that mean depth times the length of the edge  92  that is visible within the image. 
     In the illustrated example, the length of the edge (i.e., the sum of the “x” and “y” dimensions) of the bounding box  96  in  FIG. 10A  is about twice as long as the length of the edge (i.e., the sum of the “x” and “y” dimensions) of the bounding box  96  in  FIG. 10B . The bounding box  96  in  FIG. 10A  contains twice as many empty pixels  98  compared to the box in  FIG. 10B . This illustrates that the number of empty pixels  98  within a given bounding box  96  is proportional to the length of the edge (i.e., the sum of the “x” and “y” dimensions) of the bounding box  96 . 
       FIGS. 11A-D  further illustrate this proportionality. The indentations of empty pixels  98  in the fiducial mark edge  92  may be larger in some portions of the edge  92  than in others. However, indentions or pixels  98  have a certain average depth. Empty pixels or areas  98  within the bounding box  96  can be rearranged by distributing the empty areas  98  along a perimeter of the box  96  until empty areas  98  are evenly distributed at this average depth. 
     For example, a triangle shaped empty section  111  can be moved to corresponding section  112  within the rectangular area extending along the boundary lines  94  and  95 , and the triangular section  112  is at a certain depth. This process can be repeated for other indentations of empty pixels  98  until the empty pixels  98  are distributed along an edge of the fiducial mark at the same depth. The result of this process is shown  FIG. 11B . Empty spaces  98  in the other image shown in  FIG. 11C  can be reorganized in a similar manner, the result of which is shown in  FIG. 11D . Thus,  FIGS. 11A-D  further illustrate that the area of the empty regions  98  within the bounding box  96  equals the average depth of the empty area  98  multiplied by the length of the edge (sum of “x” and “y” dimensions) of the box  96 . 
     Embodiments of the invention advantageously utilize this proportional relationship of changes in the numbers of empty pixels  98  relative to the dimensions of a bounding box  96  defined by boundary lines  94  and  95  for a given pixel or point P to more effectively locate fiducial marks  16  in the presence of air bubbles  54  and other debris that overlaps the fiducial mark  16 . Embodiments of the invention achieve these advantages by locating the fiducial mark  16  by selecting a point or pixel P on the specimen slide  14  that: 1. maintains an acceptable number of empty pixels  98  within the bounding box  96  defined by pixel P, e.g., below a threshold number that varies with the dimensions of the bounding box  96 , and 2. maximizes the area or size of the bounding box  96  which, in turn, may also maximize the number of dark pixels  97  within the bounding box  96 . 
     More specifically, referring to  FIGS. 12 and 13 , according to one embodiment, a method  120  of locating a fiducial mark  16  includes identifying a group or subset of image pixels  130  as candidate pixels  132  in step  1205 . A candidate pixel  132  is a pixel that defines an area or bounding box  96  (based on boundary lines  94  and  95  extending from the pixel) containing an acceptable number of empty pixels  98  for the dimensions of the bounding box  96 . According to one embodiment, a candidate pixel  132  is a pixel that defines a bounding box  96  containing an acceptable number of empty pixels  98  as a proportion of or relative to the perimeter of the bounding box  96 . 
     A candidate pixel  132  may or may not be a pixel that is ultimately used to locate a fiducial mark  16 . Depending on the image, there may be one, a few or many candidate pixels  132 . For example, an average image of about two million pixels may contain about five hundred thousand candidate pixels  132 . Thus,  FIG. 13  is provided for purposes of illustration to generally show that a subset of image pixels  130  is identified as candidate pixels  132 . Additionally, persons skilled in the art will appreciate that whether a pixel is a candidate pixel  132  can vary depending on, for example, the brightness value or cutoff that is used to distinguish a fiducial mark or dark pixel  97  from an empty pixel  98 . 
     Having identified a set or group of candidate pixels  132 , in step  122 , one candidate pixel  134  is selected to locate the corner of the fiducial mark  16 . According to one embodiment, the selected candidate pixel  134  is the candidate pixel that defines largest bounding box  96 , e.g., the bounding box  96  with the largest area or the largest perimeter. 
     In one embodiment of the invention, steps  121  and  122  can be performed such that all candidate pixels  132  are first identified, and then one candidate pixel  134  of all of the identified candidate pixels  132  is selected. In an alternative embodiment, the step  121  and  122  can be combined by generating the candidate pixels one by one and storing only the best candidate pixel  134  as the currently selected candidate pixel  134 . The stored candidate pixel  134  may be replaced by a new candidate pixel if the new candidate pixel defines larger bounding box  96 . 
     Referring to  FIG. 14 , a method  140  according to one embodiment of the invention includes selecting a pixel of the image  130  in step  141 . In step  142 , dimensions of the area or boundary box  96  defined by boundary lines  94  and  95  extending from the selected pixel are determined. The dimension can be the perimeter of the bounding box or a portion of the perimeter. For example, referring again to  FIG. 9 , in the illustrated embodiment, one boundary line  94  is a horizontal line with a length “x” and the other boundary line  95  is a vertical boundary line with a length “y” so that the perimeter of the bounding box area is 2*(x+y). In step  143 , the number or area of empty pixels  98  contained within the defined bounding box  96  is determined. Step  143  may involve, for example, counting the number of empty pixels  98  within the bounding box  96  and/or calculating an area of empty pixels  98 . 
     In step  144 , a determination is made whether a threshold number of empty pixels  98  or empty area is satisfied. According to one embodiment of the invention, step  144  involves calculating a ratio and comparing the ratio to a threshold. In one embodiment, the ratio is (number of empty pixels)/(perimeter of area), e.g., (number of empty pixels)/(2(x+y)). Thus, step  144  involves determining whether the ratio value is greater than or less than a certain threshold value. This is equivalent to determining whether (number of empty pixels)/(x+y) is greater than a threshold value that is twice as high. In step  145 , if the threshold is satisfied, e.g., if the value of (number of empty pixels/(x+y) is less than a certain threshold value, then the pixel is selected or identified as a candidate pixel  132 . If the threshold is not satisfied, then in step  146 , the pixel is not selected as a candidate pixel  132  and is discarded. 
     In step  147 , a determination is made whether additional pixels of the image  130  should be processed. If so, then steps  141 - 147  can be repeated for each additional pixel. If not, and all of the image pixels (or all of the necessary image pixels) have been processed, then in step  148 , one candidate pixel  134  of the group or set of identified candidate pixels  132  is selected. The selected candidate pixel  134  is used to locate the corner of the fiducial mark  16 . According to one embodiment, the selected candidate pixel  134  is the candidate pixel that defines a bounding box  96  having the largest area. 
     Thus, embodiments of the invention may utilize a ratio to identify candidate pixels  132  that define a constraint on the size of the bounding box  96  defined boundary lines  94  and  95  so that the number of empty pixels  98  contained within the bounding box  96  should be no greater than a multiple of the perimeter (or other dimension) of the bounding box  96 . The point at the corner location that is selected should define the largest area of the bounding box subject to this “empty pixel” constraint. According to one embodiment, this can be done by selecting the (x, y) location that maximizes x*y (i.e., the size or area of the bounding rectangle  96 ) while, at the same time, the number of empty pixels  98  is less than a certain value d*(x+y), where d is the expected mean indentation depth. 
     Thus, a number of empty pixels  98  in a first bounding box is counted or calculated, a number of empty pixels  98  in a second bounding box is counted or calculated, and so on, for each pixel of the image so that a ratio of a number of empty pixels relative to a size or dimension of the bounding box can be calculated to determine whether a pixel is a candidate pixel  132 . One manner of determining the number of empty pixels  98  is to manually count the number of empty pixels  98  within a bounding box  96  defined by boundary lines  94  and  95  extending up and to the left of given (x, y) point or pixel in the image. This can be done for each pixel independently, and will take an amount of time proportional to the square of the number of pixels. Alternatively, according to one embodiment of the invention, the number of empty pixels  98  within a box  96  defined by a certain point or pixel can be determined based on a previous count of empty pixels  98  within a different box  96  defined by a different point pixel; this makes the amount of time necessary to process a set of pixels proportional to the number of pixels instead of the number squared, resulting in more efficient analysis. 
       FIGS. 15-19  illustrate one embodiment of the invention, in which the number of empty pixels  98  for purposes of calculating the ratio (number of empty pixels)/(x+y), used to identify candidate pixels  132 .  FIGS. 15-19  show an example image comprising a grid of pixels. Pixels corresponding to the fiducial mark  16  paint are shaded. A light pixel  98  may be surrounded by dark pixels  97  when, for example, that particular light pixel  98  is not painted. In the illustrated grid, an x−y coordinate system is applied to the pixels so that (0, 0) is at the top left corner of the image  130 . 
     Referring to  FIG. 15 , a first point P 1  at the lower right corner of a first dark pixel  97  is identified by (x1, y1). The bounding area  96  defined by horizontal and vertical boundary lines  94 ( 1 ) and  95 ( 1 ) extending upwardly and to the left from P 1  includes one pixel, which is a dark pixel  97 . There are no empty pixels  98  in the area  96  defined above and to the right of P 1 . Similarly, a second point P 2  is identified by (x2, y2). The bounding area  96  defined by horizontal and vertical boundary lines  94 ( 2 ) and  95 ( 2 ) extending upwardly and to the left from P 2  includes two pixels. One pixel is the dark pixel  97  that was previously discussed with reference to point P 1 . The other pixel is the “next” pixel that is introduced into the area  96  when the next point P 2  is selected. The next pixel is also a dark pixel  97 . Thus, there are no empty pixels  98  in the area  96  defined above and to the right of P 2 . Further, a third point P 3  is identified by (x3, y3). The bounding area  96  defined by horizontal and vertical boundary lines  94 ( 3 ) and  95 ( 3 ) defined by P 3  includes three pixels including the two pixels previously considered. The third pixel is the “next” pixel that is introduced into the area  96  when the next point P 3  is selected. The third or next pixel is also a dark pixel  97 . Thus, there are no empty pixels in the area  96  defined above and to the right of P 3 . 
     As shown in  FIGS. 16-18 , grid cells can be labeled with a number, which represents the number of empty pixels above and to the left of a particular point. While the empty pixel count could be calculated independently for each pixel, according to one embodiment, a method of counting the number of empty pixels is done using previous counts of empty pixels, which can be significantly faster than counting independently, particularly considering that an image can have, for example, two million pixels. 
     For example, referring to  FIG. 16 , a “0” value can be assigned to the first pixel to represent that there are no empty pixels  98  above and to the left of P 1 . Similarly, a “0” value can be assigned to the second pixel to represent that there are no empty pixels  98  above and to the right of P 2 , and a “0” can be assigned to the third pixel to represent that there are no empty pixels  98  above and to the right of P 3 . This process can continue for additional pixels in the row. 
       FIG. 16  illustrates a point P 6  that defines an area  96  defined by boundary lines  94 ( 6 ) and  95 ( 6 ) extending from the point P 6 . The area  96  contains five dark pixels  97  (each of which is assigned a “0” value) and an additional or next pixel as a result of selection of the next point P 6 . This pixel, in contrast to previously discussed pixels, is an empty pixel  98 . Thus, there is one empty pixel  98  above and to the left of point P 6 , and a value of “1” can be assigned to this pixel. Similarly for P 7 , the area defined by boundary lines extending from P 7  encloses the six previously analyzed dark pixels (five of which were assigned a “0” value; one of which was assigned a “1” value) and a new or next pixel. In this case, the next pixel is a dark pixel  97 . Thus, there are no new empty pixels  98  in the area  96  defined by boundary lines extending from point P 7 . As a result, a value of “1” can also be assigned to this seventh pixel since the empty pixel count remains the same. This process continues for each pixel of the image. 
       FIG. 17  further illustrates how numbers can be assigned to represent empty pixel count values.  FIG. 17  only shows the pixel count values above and to the right of a point P for purposes of illustration and explanation. A shown in  FIG. 17 , boundary lines  95  and  95  extending to the left and upwardly from point P define an area  96  that includes 24 pixels. Three of these 24 pixels are empty pixels  98  and, therefore, the next pixel introduced by point P is assigned a value of “3” to indicate there are three empty pixels  98  in the area  96  defined by boundary lines  94  and  95  extending from point P. 
     Similarly, referring to  FIG. 18 , consider the pixel indicated as “Next point” in  FIG. 18 . Every pixel above or to the left of “Next Point” is contained in the region defined either by the pixel immediately above “Next Point” or the pixel immediately to the left of “Next Point”. If the empty counts for these two neighbor pixels are added together, all the pixels in the region defined by the pixel diagonally up and to the left of “Next Point” will be included twice in the sum. Therefore, the number of empty pixels above and to the left of “Next Point”, not counting “Next Point”, is equal to the count for the pixel above “Next Point”, plus the count for the pixel to the left, minus the count for the diagonal pixel. If “Next Point” is empty, an additional value of one should be added to this sum.  FIG. 19  further illustrates the “Next Point” analysis illustrated in  FIG. 18 . 
     In more mathematical terms, let E(x, y) be the number of empty pixels above and to the left of the pixel at image coordinates (x, y); that is, the number of empty pixels (x′, y′) for which x′≦x and y′≦y. Let E(x, y) be zero if (x, y) is not a point within the image. Then, for each (x, y) in the image, E(x, y) is equal to E(x−1, y)+E(x, y−1)−E(x−1, y−1) plus one if the pixel at (x, y) is itself empty. 
     According to one embodiment of the invention, a method locating a fiducial mark  16  by selecting a point or pixel that maximizes the area of dark or fiducial mark  97  while maintaining the number of empty or light pixels  98  (determined, e.g., by the method described above) below a certain number can be expressed as: 
                                            let             I(x, y) = grayscale value of pixel at coordinates (x, y) in image,             T = threshold grayscale value below which pixel is considered             part of mark,             E(x, y) = count of pixels (x′, y′) for which x′ &lt;= x, y′ &lt;= y,             and I(x′, y′) &gt;= T.             M = constant multiplier,           then fiducial mark corner is at the coordinates (x1, y1) that maximize           x1*y1,             subject to the constraint that             E(x1, y1) ≦ M * (x1 + y1) or equivalently,             E(x1, y1) / (x1 + y1) ≦ M.                        
wherein E (x,y) is calculated as follows:
 
     
       
         
           
               
             
               
                   
               
             
            
               
                 let 
               
               
                   B(x, y) = 1 if I(x, y) &gt;= T, 0 otherwise, 
               
               
                   E(x, y) = 0 when x &lt; 0 or y &lt; 0 
               
               
                 When x &gt;= 0 and y &gt;= 0, 
               
               
                   then E(x, y) = E(x − 1, y) + E(x, y − 1) − E(x − 1, y − 1) + B(x, y). 
               
               
                   
               
            
           
         
       
     
     Thus, if E values in the image are calculated row by row (y=0 to y=image height), then by moving from left to right (x=0 to x=image width) within each row, each value representing the number of empty pixels  98  can be based on previously computed values, thereby reducing the computational complexity from O(n 2 ) in the number of pixels to O(n). 
     This is expressed in other terms by the following pseudo code: 
     
       
         
           
               
               
             
               
                   
                   
               
             
            
               
                   
                 bestX = 0; 
               
               
                   
                 bestY = 0; 
               
               
                   
                 for y = 0 to (image height − 1) 
               
               
                   
                   for x = 0 to (image width − 1) 
               
               
                   
                     E(x, y) = E(x−1, y) + E(x, y−1) − E(x−1, y−1) + B(x, y); 
               
               
                   
                     if E(x, y) ≦ M * (x + y) and x * y &gt; bestX * bestY 
               
               
                   
                       bestX = x; 
               
               
                   
                       bestY = y; 
               
               
                   
                     end if 
               
               
                   
                    end for 
               
               
                   
                 end for 
               
               
                   
                 return (bestX, bestY); 
               
               
                   
                   
               
            
           
         
       
     
     With the above-described methods, various embodiments of the invention can be used to quickly determine the number of empty pixels  98  contained within an area or bounding box  96 , thereby allowing the ratio of (number of empty pixels)/(x+y) to be calculated to determine whether a particular pixel is a candidate pixel  132 . Persons skilled in the art will appreciate that although  FIGS. 15-19  illustrate one method of determining the number of empty pixels  98  within a given bounding box or area  96 , other processing methods and techniques can also be used for this purpose. Accordingly, the example of determining empty pixel counts with a type of dynamic programming is provided for purposes of illustration and explanation, and other suitable methods can also be used to count the empty number of pixels within a given area or bounding box  96 . 
     Having located the fiducial mark  16 , embodiments also improve the manner in which the imaging station  18  focuses on the located fiducial mark  16  may be improved. In a well focused image of a fiducial mark  16 , the edge  92  of the mark  16  is an abrupt transition from dark to light. If the image is poorly focused, however, the transition is more of a blurred gradual gradient. The width of this blurred gradient region depends on the distance from the ideal focal plane and can, therefore, be used as a focus score for automatic focusing processes. 
     According to one embodiment, the width of the blurred region can be determined by using embodiments for locating a fiducial mark (e.g., the method shown in  FIG. 14 ) and adjusting the intensity threshold between the “mark” and “empty” pixels. More particularly, according to one embodiment, focus improvements are achieved by performing, e.g., the method  140  shown in  FIG. 14 , using two different brightness or threshold levels that denote whether a pixel is an empty pixel  98  or a fiducial mark pixel  97 . For example, the method  140  can be performed based on a brightness or threshold value of 192 so that pixels having gray values less than or equal to 192 are considered fiducial mark pixels  97 , and pixels having gray values above 192 are empty pixels  98 , and also at another brightness or threshold level, e.g., 64, so that pixels having gray values less than or equal to 64 are considered fiducial mark pixels  97 , and pixels having gray values above 64 are empty pixels  98 . Persons skilled in the art will appreciate that other brightness threshold values can be utilized. 
     Use of different brightness or threshold values results in an automatic focusing system or process selecting different candidate pixels which, in turn results in different bounding boxes that are separated by a distance “d”. This distance can be used to indicate the focus quality and allow the image with the best focus to be selected. The presence of dust on top of the cover slip does not affect the measurement of blur, even though dust or other debris may be very sharply focused. 
     Thus, referring to  FIG. 20 , one embodiment of a method for focusing on a fiducial mark includes selecting a candidate pixel from a group or set of candidate pixels  132  having maximum fiducial mark area during processing at a first empty pixel brightness or threshold level in step  201 . In step  202 , a boundary box or area  96  defined by the selected candidate pixel is determined based on the first threshold level. In step  203 , a candidate pixel is selected from a group or set of candidate pixels  132  having maximum fiducial mark area during processing at a second empty pixel brightness or threshold level. In step  204 , a boundary box or area defined by the selected candidate pixel is determined based on the second empty pixel threshold level. Then, in step  205 , the degree to which a fiducial mark  16  is out of focus is determined based on the distance between the two boundary boxes. In step  206 , the image with the best focus can be selected. If necessary, in step  207 , the imaging microscope  26  can be adjusted to further improve the focus on the fiducial mark  16 . 
       FIGS. 21-23  illustrate one example of how embodiments of the invention can be implemented to improve focus quality. In the illustrated embodiment, empty pixel threshold gray values of 64 and 192 were utilized, but persons skilled in the art will appreciate that other values can also be utilized. Referring to  FIG. 21 , an inner bounding box  210  is defined by boundary lines extending from a candidate pixel selected using a higher empty pixel threshold so that fewer pixels are selected as “dark” pixels  97 . An outer bounding  212  is defined by boundary lines extending from a different candidate pixel selected using a lower empty threshold so that more pixels are selected as “dark” pixels  97 . In the illustrated example, the inner and outer boxes  210  and  212  are separated by a small distance “d”. Thus, in this example, the different empty pixel threshold values of 64 and 192 do not significantly alter the resulting location of a fiducial mark  16 . However, as the fiducial mark  16  goes farther out of focus, the distance “d” between the two bounding boxes increases. 
     Referring to  FIG. 22 , the fiducial mark  16  is more out of focus compared to  FIG. 21 . The inner boundary box  220  corresponds to a fiducial mark  16  that is located when a higher empty pixel threshold is used so that fewer pixels are selected as “dark” pixels  97 . The outer boundary box  222  corresponds to a fiducial mark  16  that is located when a lower empty pixel threshold is used so that more pixels are selected as “dark” pixels  97 . Comparing  FIGS. 21 and 22 , the distance “d” between the boxes  220  and  222  is larger than the distance “d” between boxes  210  and  212  as a result of reduced quality of focus on the fiducial mark  16 . 
     Similarly, referring to  FIG. 23 , the fiducial mark  16  is even more out of focus, and the dust  52  below the fiducial mark  16  is more in focus compared to  FIGS. 21 and 22 . The inner boundary  230  corresponds to a fiducial mark  16  that is located when a higher empty pixel threshold is used so that fewer pixels are selected as “dark” pixels  97 . The outer boundary box  232  corresponds to a fiducial mark  16  that is located when a lower empty pixel threshold is used so that more pixels are selected as “dark” pixels  97 . Comparing  FIG. 23  to  FIGS. 21 and 22 , the distance “d” between the boxes  230  and  232  becomes increasingly larger as the fiducial mark  16  is increasingly out of focus. However, the dust  52  near the bottom of the fiducial mark  16  does not affect the focus measurement even with the larger in-focus dust particles shown in  FIG. 23  since embodiments advantageously ignore all objects except the fiducial mark  16  corner. 
     These advantages are further illustrated in  FIG. 24 , which is a chart illustrating how embodiments improve the process of focusing on a fiducial mark. In  FIG. 24 , the dotted line  80  contains a false peak ( FIG. 8 ) that is produced as a result of focusing on dust  52  rather than a fiducial mark  16  when using known systems. Embodiments of the invention advantageously eliminate the false peak  80  as demonstrated by the solid line  240 , which represents the improved focus score achieved using one embodiment. The solid line  240  has a peak at 0 microns, i.e., at the correct focal plane  70 , without a false peak  72 , as in  FIG. 7 , which illustrates focusing known systems. 
     In another embodiment of the invention, the scale of blurring of the fiducial mark  16  can be calculated by comparing mark  16  locations or boundaries that are measured at different light/dark thresholds. For example, referring to the chart shown in  FIG. 24 , the solid line  240  is the Euclidean distance between the locations measured at thresholds of 64 and 192, inverted and scaled to align with the Brenner function. Using this distance for the fiducial mark  16  score, the imager microscope  26  and the review microscope  38  would focus on the true fiducial mark  16  focus plane rather than the higher, false focus plane  72  caused by dust and debris  52 . 
     Although particular embodiments have been shown and described, it should be understood that the above discussion is not intended to limit the scope of these embodiments. Various changes and modifications may be made without departing from the scope of embodiments. For example, candidate pixels could be chosen based on some other function of the empty pixel count. The best candidate pixel could be chosen by maximizing the perimeter or the number of dark pixels contained instead of by maximizing area. Additionally, although the specification has described embodiments with reference to fiducial marks, persons skilled in the art will appreciate that embodiments can also be used to locate and focus on other specimen slide marks. Further, although embodiments have been described with reference to rectangle and box shapes, persons skilled in the art will appreciate that embodiments can be implemented with other shapes if desired. 
     As a further example, embodiments can be applied where there is a dark object in a known location on a slide. The degree of blur of that object could be found by measuring its size (when thresholded at two different levels as discussed above) to provide a focus score that is not influenced by dust on the slide. Thus, embodiments are intended to cover alternatives, modifications, and equivalents that may fall within the scope of the claims.