Patent Publication Number: US-2009238435-A1

Title: Multi-Exposure Imaging for Automated Fluorescent Microscope Slide Scanning

Description:
BACKGROUND OF THE INVENTION 
     The present invention relates to a method and a system for generating a composite image from multiple images which have a large variation in pixel value brightness, such as, for instance, microscope slides containing fluorescently stained material. In particular, the present invention provides a method and a system for imaging and combining multiple exposure images into a single well contrasted image while ensuring that the areas of low fluorescent intensity and of high fluorescent intensity are combined in such a way as to preserve good contrast on the entire combined image. 
     Fluorescent microscopy has become an essential tool in biomedical sciences due to its superb contrasting capability that is not readily available with the traditional optical microscopy. Fluorescent microscopy is based on the underlying physical phenomenon of fluorescence, which is a property of some materials to shift the light wavelength between the incoming, i.e. specimen excitation, light spectrum and the outgoing, i.e. specimen emission, light spectrum. The emission of light through the process of fluorescence is nearly simultaneous with the excitation due to a relatively short time delay between photon absorption and emission. The delay is usually less than a microsecond in duration. 
     Other inorganic specimens were found to fluoresce when irradiated with ultraviolet excitation light. Subsequently, a discovery was made of a class of biological fluorescents which selectively bind to cell components in the tissue thus “staining” them. These biological fluorescent “stains” are also called fluorochromes. Different fluorochromes are often highly specific in their propensity to bind to the cell components. An array of these biological fluorochromes can be distributed over a biological specimen as to tag and distinguish cells and cellular components, making possible their optical identification with a high degree of specificity. The widespread growth in the use of fluorescence microscopy is closely linked to the development of the new synthetic fluorochromes and the discovery of the naturally occurring fluorochromes. These fluorochromes have known excitation and emission bandwidths, and their biological binding targets are well defined. 
     Capturing a well contrasted, in-focus fluorescent image of the microscopic slide is a non-trivial task. In addition to wavelength differences in the excitation and emission light, the intensity of the emitted fluorescent light is usually several orders of magnitude weaker than that of the excitation light. Therefore, it is desirable to separate the much weaker emission light from the excitation light. This is usually achieved through the use of the specific wavelength filters within a fluorescent microscope system (described in the Detailed Description of the Invention). However, even the best filtering of the excitation and the background light still results in the digital images of the fluorescent slides that have a high dynamic range of the intensities. Such high dynamic range can exist within individual digital frames; across multiple digital frames; as well as across multiple fluorescent sample slides. The manual adjustments of the exposure time and focus would be a highly impractical solution for at least two reasons. First, since the stains have a limited life-time (which actually decreases with the intensity of the incoming excitation light) there is no room for the time consuming adjustments of the focus and exposure time. Second, the modem biology diagnostic labs require the high-throughput, unattended, automatized methods for the acquisition of the in-focus, well contrasted images. That requirement is incompatible with the slow and laborious manual image acquisition adjustments. 
     The need for the well focused and well contrasted fluorescent digital images has been recognized in the imaging industry for some time. Some existing methods attempt to calibrate the acquired digital images to an identical level of brightness and contrast, followed by an integration of the pixel intensity values across each image in order to arrive at the fluorescent stains&#39; concentration in the cell. However, this method results in an average, across the image stain concentration value, and not in a properly contrasted image with well preserved areas of the low and high fluorescent intensities. 
     Some other existing methods deal only with the auto-focusing of the fluorescent imaging system by analyzing the pixel intensity statistics either over the entire image or over the multiple sub-regions of the image. The auto-focus calculations are based on the pixel intensity values. Proper focus value is chosen such that it maximizes either the global or the local digital image contrast value. Therefore, those methods determine an optimal focus position based on the entire slide or a sub-region of the slide, which helps achieve a proper focusing on the fluorescent image, but the methods still do not produce a well contrasted composite image of the slide. 
     Yet some other methods record digital images before and after staining the slides with fluorochromes. Next, the images obtained before and after staining are compared pixel by pixel. If the pixel intensity difference exceeds a certain pre-established threshold, then that pixel is declared to indicate the location of a stain. Thus, a binary map of pixels is established over the image: no stain in the pixel location or stain present in the pixel location. That method at best results in a map of stain locations over the slide, but all the contrast and intensity variations over the slide are lost in the process. 
     Some other methods use multiple images to arrive at a composite image of a single slide. However, those methods perform straight forward averaging of the corresponding pixel intensities over the multiple exposures. The pixel intensities are typically scaled by a fixed coefficient across the entire image in order to avoid the summation caused overflow, but no intelligent weighting is done while arriving at the pixel intensity average. Therefore, those methods also do not produce the desired well contrasted digital image of the slide. 
     Thus, there exists a need for systems and methods of automated fluorescent image auto-focusing and digital image acquisition that can produce a well contrasted image while preserving the areas of low and high fluorescent intensities on the fluorescent slide. 
     BRIEF SUMMARY OF THE INVENTION 
     The present invention provides methods and systems for the acquisition of the digital images of a microscope slide which can have a large variation in pixel value brightness, like, for instance, fluorescent microscope slides. Multiple images of the microscope slide are acquired at differing exposure times. A weighted sum of pixel intensities is formed from the images, where the weight given to each pixel can be calculated from the localized contrast values around that pixel. A higher weighting can be given to those pixels that have higher localized contrast values. The methods and the systems can produce a well contrasted composite image with the areas of low fluorescent intensity and the areas of high fluorescent intensity at the input images being preserved on the composite image. In conjunction with the multiple image acquisition methods and systems, an auto-focusing method based on multiple exposures of the slide can be used to determine appropriate focal values for the image acquisition. 
     In one embodiment, a method of forming an image of a fluorescent microscope slide includes: acquiring an image of a fluorescent microscope slide at a first exposure level resulting in a first acquired image at a first contrast level; acquiring additional images of the fluorescent microscope slide each at an exposure level different from each other and different from the first exposure, resulting in different acquired images at different contrast levels; and forming a composite image of the fluorescent microscope slide by forming a weighted sum of the intensity values of the corresponding pixels of each of the images, whereby the composite image has a composite contrast level over the entire composite image that represents a weighted average of the contrast levels. 
     In one aspect, the weight given to each of the pixels of each of the images is calculated in part from a localized contrast measure around that pixel. 
     In another aspect, a higher weight is given to a pixel whose localized contrast is higher. 
     In another embodiment, a method of forming an image of a fluorescent microscope slide includes: using multi-exposure auto-focus at one or more points spaced at or around one or more sub-areas within the microscope slide; generating a map of auto-focus values per sub-areas of the microscope slide; acquiring an image of a fluorescent microscope slide using the map of auto-focus values at a first exposure level resulting in a first acquired image at a first contrast level; acquiring additional images of the fluorescent microscope slide using the map of auto-focus values, where the images acquired at an exposure level are different from each other and different from the first exposure, resulting in different acquired images having different contrast levels; forming a composite image of the fluorescent microscope slide by forming a weighted sum of the intensity values of each corresponding pixels of each of the images, whereby the composite image has a composite contrast level over the entire composite image that represents a weighted average of the contrast levels. 
     In yet another embodiment, a fluorescent image system includes: a source of illumination; a fluorescent microscope slide; a microscope system; a CCD camera for acquiring digitized images; and a computing unit for storing and processing the digitized images. The computing unit executes a method so as to cause: a CCD camera image of the fluorescent microscope slide to be acquired at a first exposure level resulting in a first contrast level; additional images of the fluorescent microscope slide to be acquired each at an exposure level different from each other and different from the first exposure, resulting in different contrast levels; a composite image of the fluorescent microscope slide to be formed as a weighted sum of the intensity values of each corresponding pixels of each of the images, whereby the composite image has a composite contrast level over the entire composite image that represents a weighted average of the contrast levels. 
     For a further understanding of the nature and advantages of the invention, reference should be made to the following description taken in conjunction with the accompanying figures. It is to be expressly understood, however, that each of the figures is provided for the purpose of illustration and description only and is not intended as a definition of the limits of the embodiments of the present invention. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates a schematic diagram of an epi-illuminated fluorescence microscope system. 
         FIG. 2  illustrates a schematic diagram of a trans-illuminated fluorescence microscope system. 
         FIG. 3  shows a flowchart of the image acquisition and a composite image creation, in accordance with one embodiment of the present invention. 
         FIG. 4  shows input images and a blended output image obtained in accordance with one embodiment of the present invention. 
         FIG. 5  shows a flowchart of a method for auto-focusing which uses multiple exposures. 
         FIG. 6  shows a flowchart of Ariol system&#39;s imaging method. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     The embodiments of the present invention are directed towards methods and systems for the image acquisition and its related postprocessing using multiple images acquired at different exposure times. The present invention is especially well suited for the microscope slides that have large variation in brightness, like, for instance, fluorescent microscope slides. The present invention generates a well focused and well contrasted composite image from the images acquired at different exposure times. 
     The details of an exemplary embodiment of the present invention are explained with reference to  FIGS. 1-6 . The exemplary embodiment is described with reference to fluorescent microscope slides, but it will be clear to a person skilled in the art that the present invention can be used for other types of slides having large variations in brightness. 
       FIG. 1  shows an epi-illuminated fluorescence system which is used for acquiring and storing digital images of the microscopic fluorescent slides. As explained above, one of the problems in capturing well contrasted digital images of the fluorescent slides is that the intensity of the emitted fluorescent light is usually several orders of magnitude weaker than that of the excitation light. Therefore, it is desirable to prevent the excitation light from reaching the camera sensor. This is usually achieved by selective wavelength filtering because the excitation and emission light have different wavelengths. The selective wavelength filtering separates the much weaker emission light from the much stronger excitation light, while also directing the emission light to the camera sensor. The details of the filtering scheme that can be used in a fluorescent microscope system are explained below. The fluorescent microscope system  10  has source of light  25  (usually a mercury or a xenon lamp) which contains the stain excitation wavelengths. The light from source  25  passes through excitation wavelength filter  12 , which can be one of the several filters on a rotatable filter wheel. Wavelength filter  12  transmits the light wavelength band needed for a particular stain excitation, while blocking other wavelengths. Wavelength based optical filters are widely known, and a person skilled in the art would know how to choose a filter with the required characteristics. After passing through excitation wavelength filter  12 , the filtered light from the source  25  next impinges on dichromatic beamsplitting mirror  15 , which is a specialized interference filter that efficiently reflects the shorter wavelengths and efficiently transmits the longer wavelengths. (The excitation light is denoted by the open arrows, while the emission light is denoted by the solid arrows.) The dichromatic beamsplitting mirror  15  is tilted at 45° angle with respect to the incoming excitation light. Since the incoming filtered excitation light has a shorter wavelength, it is reflected by beamsplitting mirror  15  toward objective lens  17 , which focuses the excitation light onto fluorescence microscope slide  10 . After being illuminated with the light at the excitation wavelength, the stains in slide  10  emit the light at the emission wavelength, which is longer than the excitation one. The emission light travels through objective lens  17 , and impinges onto dichromatic beamsplitting mirror  15 , which was selected such as to pass longer wavelengths through and toward barrier filter  20 . It should be noted that the selection of a specific beamsplitting mirror  15  is dictated primarily by the excitation and emission wavelengths, which are stain dependent. Most of the excitation light that was scattered off slide  10  or other parts of the setup toward beamsplitting mirror  15  is reflected back toward light source  25 , but a minute quantity often passes through. Since the intensity of the excitation light is much stronger than that of the emission light, even a very small percentage of the scattered excitation light reaching the camera could contaminate the image. Therefore, the role of barrier filter  20  is to selectively transmit light at the excitation wavelength toward image plane  30  of digital camera  32 , while blocking the scattered excitation light. An image is formed on image plane  30 , and is acquired by digital camera  32 . Once the image is captured by digital camera  32 , it can be transferred to computing unit  35  for storage and further processing. 
     The pairs of dichromatic beamsplitting mirror  15  and barrier filter  20  are typically assembled into optical blocks (often called the cubes), which can be easily interchanged to achieve the proper, stain dependent filtering of the excitation and emission light. This interchangeability of cubes  40  and  40 ′ is denoted with arrows  42 . Sometimes excitation wavelength filter  12  is added to the cube configuration, thus making filter  12  also interchangeable by replacing cube  40 . 
     Epi-illuminated fluorescence systems typically can produce images with very high brightness levels. However, the design and manufacturing of a dual bandwidth filter, like dichromatic beamsplitting mirror  15 , can be difficult and expensive. Also, multiple cubes may be needed in the fluorescent microscope system like the one shown in  FIG. 1 , each cube having different dichromatic mirror. Therefore, a fluorescent microscope system like the one depicted in  FIG. 2  is sometimes used, because it uses only the single bandwidth filters, which are less expensive. This type of the microscope system is known as the trans-illumination fluorescence system. Here, source  25  emits excitation light that passes through rotatably mounted excitation wavelength filter  12 . The excitation light is reflected by mirror  152 ; it passes through condenser lens  155 ; and then it impinges on microscope slide  10 . The light at both the excitation wavelength (denoted by solid arrows) and the emission wavelength (denoted by open arrows) passes through objective  17 . Rotatably mounted emission wavelength filter  160  selectively transmits the emission wavelength of the interest, i.e. the one that was emitted by the stain of interest. An image is formed on image plane  30 , and is acquired by digital camera  32 . The image can now be transferred to computing unit  35  for storage and further processing. Notice that the role of the cube from the epi-illuminated microscope system, as illustrated on  FIG. 1 , is replaced by a plurality of emission filters  160  that can be mounted on a rotating wheel. No dual band filter, as in dichromatic beamsplitting mirror  15 , is needed. One disadvantage of the trans-illumination fluorescence system is that the illumination is not as bright as with epi-illumination system because condenser  155 , which has a lower numerical aperture than the objective  17 , is used to focus the light onto the sample. However, an appropriately chosen exposure level can alleviate this disadvantage, as explained in detail below. 
     Where multiple stains are used, and especially in multi-tissue setting, the camera exposure setting for one stain may not be preferred for another stain. For example, the exposure setting which is suitable for one stain or one type of tissue may be too low for another stain or tissue, resulting in a poorly defined tissue on the image and also in the inability to focus on the under-exposed stain. Conversely, an appropriate exposure for one stain or tissue may lead to an overexposure for another one, resulting in the pixel intensity overflow, inability to clearly identify the tissue, and also an out of focus image. Even with a single stain, the appropriate exposure duration is not known a-priori, and the problems with the appropriate exposure can persist. A method for overcoming the underexposure and overexposure difficulties described above is illustrated with reference to  FIG. 3 , which describes image acquisition and postprocessing according to one embodiment of the present invention. Such a method may be computer-implemented and executed by computing unit  35 . Fluorescent microscope slide imaging is used to explain the method, but other microscope slides having large variation in brightness can also be used with the method. An epi-illumination or trans-illumination or a similar system can be used with the method. 
     At step  110  a digital camera acquires an image of the tissue. The image of an entire microscope slide or of a particular sub-region of the slide can be acquired. A low exposure time is used when step  110  is executed for the first time. Step  110  will be repeated for the appropriate number of times, based on the decision mechanism explained below. Digital images are saved in a processing unit, like, for instance, processing unit  35  in  FIGS. 1  or  2 . 
     At step  120  the camera exposure time is increased. Good exposure values that would result in good image contrast, are not known a-priori. Therefore, the method starts at some predefined low exposure time, and then increases the exposure time of the camera for every subsequent image acquisition. 
     At step  130  a check can be performed to verify if the predetermined maximum exposure value is reached. If the predetermined maximum exposure value is not reached, then another image of the slide or the sub-region of the slide is acquired in step  1   10 . If the maximum exposure value is reached, then the method exits the loop, and proceeds to step  140 . 
     At step  140  the stored digital images are accessed and processed by, for example, processing unit  35 . A variation is calculated over a pre-defined neighborhood of the pixel for each pixel in the image. Typically, a 21×21 pixel area is centered over the pixel of interest, but other suitable pixel areas may be used, as long as the resulting blended images end-up having good smoothing of the pixel intensities. In the alternative, a localized histogram around a pixel can be generated, and a variance of the histogram per pixel is measured and smoothed using the process described below. 
     Special treatment is accorded to the pixels close to the boundary of the image. For instance, for a pixel that is only 8 pixels away from the right hand boundary of the image, a corresponding 17×21 pixel area can be defined to calculate the relevant values. For a pixel that is only  6  pixels away from the upper boundary of the image, a corresponding 21×13 pixel area can be defined. Methods of image edge treatment are well known to a person skilled in the art of image processing, and such a person would know of a variety of suitable edge treatments. Further details of step  140  are set forth below. 
     First, the difference between the brightest and the darkest pixel within the 21×21 pixel area is found. The difference is called Range Image, or RI for short. There is one RI value for each pixel in every image. Next, still at step  140 , a Smoothed Range Image, or SRI for short, is calculated by finding a mean value of all the RI values per pixel in the 21×21 area centered around that pixel. This is done by summing up all of the RI values in the 21×21 pixel area, and dividing the resulting sum by 441 (i.e., 21×21). The process is repeated for other pixels in that image, and then for the pixels in the remaining images. 
     Eqs. 1 and 2 show the high level algebra involved in step  140 . The equations below should be understood as a high level algorithm of the image processing in the present invention, and not as a rigorous, self-contained mathematical implementation. A person skilled in the art would know a variety of computer coding implementations for the high level algorithm below. 
     In the first step of the algorithm, Range Image is calculated for every pixel in every image: 
       RI=|MAX( I   i,j   −I   k,l )|  (1) 
     where I is a pixel value (i.e. brightness or light intensity at that location), i,j and k,l are pixel indices belonging to the same 21×21 pixel region. 
     Next, Smoothed Range image is calculated according to Equation below. 
     
       
         
           
             
               
                 
                   SRI 
                   = 
                   
                     
                       1 
                       
                         N 
                         * 
                         N 
                       
                     
                      
                     
                       
                         ∑ 
                         
                           i 
                           = 
                           1 
                         
                         N 
                       
                        
                       
                         
                           ∑ 
                           
                             j 
                             = 
                             1 
                           
                           N 
                         
                          
                         
                           RI 
                           
                             i 
                             , 
                             j 
                           
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   2 
                   ) 
                 
               
             
           
         
       
     
     where N=21 for the non-edge pixel. 
     Still at step  140 , Range Squared (RS) is determined by calculating SRI 2  (SRI multiplied by itself) for all pixels in each of the images. Thus, for every pixel in every image: 
       RS=SRI*SRI   (3) 
     The per-pixel RS values are stored for every image inside computing unit  35 . The above RS values are used in the subsequent smoothing by preventing the undesired transitions that otherwise may be artificially introduced in the final blended image if the weighted sum includes image data exclusively from one image. 
     In steps  150 - 170 , the calculations are done over a fixed pixel location in each of the images from the input set. Once the calculations in steps  150 - 170  are completed for a particular pixel location, they are repeated for the next pixel. At step  150 , pixel light intensity values at a fixed pixel location are multiplied by Range Squared (RS), and summed over all the images. The resulting per pixel value is called Intermediate Weighted Average or IWA. Thus, for a fixed pixel location over all the images: 
     
       
         
           
             
               
                 
                   IWA 
                   = 
                   
                     
                       ∑ 
                       
                         g 
                         = 
                         1 
                       
                       M 
                     
                      
                     
                       
                         RS 
                         g 
                       
                       * 
                       
                         I 
                         g 
                       
                     
                   
                 
               
               
                 
                   ( 
                   4 
                   ) 
                 
               
             
           
         
       
     
     where M is the number of input images that are used to produce a smoothed image, and the subscript ‘g’ denotes marching over the images for a fixed pixel location. 
     At step  160 , Range Squared (RS) values are summed-up over all the images for the same fixed pixel location to arrive at the Range Squared Sum (RSS) value, as shown in Equation (5) below: 
     
       
         
           
             
               
                 
                   RSS 
                   = 
                   
                     
                       ∑ 
                       
                         g 
                         = 
                         1 
                       
                       M 
                     
                      
                     
                       RS 
                       g 
                     
                   
                 
               
               
                 
                   ( 
                   5 
                   ) 
                 
               
             
           
         
       
     
     At step  170 , the final per pixel step, Intermediate Weighted Average (IWA) is divided by Range Square Sum (RSS) to calculate Weighted Average (WA). 
     
       
         
           
             
               
                 
                   WA 
                   = 
                   
                     IWA 
                     RSS 
                   
                 
               
               
                 
                   ( 
                   6 
                   ) 
                 
               
             
           
         
       
     
     The result of Eq. 6 is a smoothed light intensity value for one pixel location in a 2-D image. The process outlined in Eqs. 3-6 is now repeated for the next fixed pixel location, as many times as there are pixels in the image. 
     At step  180  the weighted pixel values for the same pixel location in all the images are assembled into a composite image. This step produces a single blended output image that is well contrasted over the areas of both low and high fluorescent intensities. 
     A set of input and resulting images is shown in  FIG. 4 . The three input images in  FIG. 4  suffer from distinct shortcomings. The image on the top ( 30  ms exposure) is underexposed, therefore the stains can not be clearly identified. The middle input image ( 120  ms exposure) has a proper exposure, but the contrast is too low for the stain identification. The image at the bottom (240 ms exposure) is overexposed, thus making the background area behind the stains too bright for the subsequent processing. When image smoothing as described in  FIG. 3  and Eqs. 1-6 is employed on the three input images, the resulting blended image shown on the right hand side of  FIG. 4  is produced. This blended image has a proper combination of contrast and exposure to enable a more effective stain identification in subsequent lab processing. 
     It is to be understood that the method as described above with reference to  FIG. 3  can also be executed over sub-regions of fluorescent microscope slides. A manual or automatic method of defining the sub-regions of interest can be used in conjunction with the method. The use of the method per a sub-region of a slide is usually done when the particular sub-region contains a tissue area of interest. Alternatively, the sub-regions can be defined based on their relative content of the stains. Many other methods of defining sub-regions can be used. 
     The method described above with reference to  FIG. 3  can be performed in conjunction with auto-focusing. A properly focused microscope system enables the capturing of the images at the highest contrast values, which, in turn, improves the quality of the output blended image. Performing a manual focus would be a tedious work that is not well suited for a highly automatized image acquisition process. On the other hand, using a predetermined fixed focal value for different slides could result in the inaccurate focus for some stains, because their true depth position can differ from slide to slide. Therefore, in conjunction with the multiple image acquisition methods and systems, an auto-focusing method based on multiple exposures of the slide can be used to set the proper focal values for the image acquisition. 
       FIG. 5  shows an auto-focus method according to one embodiment of the present invention. The method is applicable to either epi-illuminated or trans-illumination fluorescence systems, and also to other bright field image acquisition systems. 
     At step  210  a digital image is acquired at one exposure and one z-position of the objective lens. If the focal value of the entire slide is wanted, then the image of the entire slide is acquired. Alternatively, if the focal value of a particular sub-region of the slide needs to be determined, then an image of that sub-region is acquired. 
     At step  220  the contrast value of the acquired image is calculated. A person skilled in the art would know a number of methods to calculate contrast of an image. For example, a strip of pixels could be selected from the image to calculate differences between the neighboring pixels. The sum of the pixel intensity differences can be representative of contrast. Alternatively, a two-dimensional region of the image or even the entire image can be used to calculate pixel-to-pixel intensity differences. Calculations can be done by comparing pixel intensity not only against the immediate neighbors of that pixel, but also against the more distant pixels. Thus, a numerical value of the contrast, representing the goodness of the focus, is calculated, and is saved for the future processing. Higher contrast indicates a better focus for the images acquired over the same area of the slide. 
     At step  230  the next longer value of the camera exposure is chosen. 
     At step  240  the comparison is made between the exposure time at step  210  and the predetermined maximum exposure time. If the maximum exposure time is not reached yet, then another image per step  210  is acquired. If the maximum exposure time is already reached, then no new images are acquired at this z-position of the lens. Instead, step  250  of the method is executed. 
     At step  250  the lens is focused on the next z-position. 
     At step  260  a comparison is made between the present z-position of the lens and the predetermined value of the maximum z-position value, and an image per step  210  is acquired. If the maximum z-position value is not reached yet, then the lens is focused to the next z-position. If the maximum z-position value is reached, then no new images need to be acquired. 
     At step  270  the numerical values of the image contrast, which were obtained at step  220 , are compared. A higher contrast value corresponds to a better focus. If the entire slide was imaged, then the highest value of the contrast will determine the best z-position for the focus. For the sub-region focusing, a focus map of per sub-region focal values can be created. The focal values can be used to pre-adjust the focus when acquiring digital images as in the method described with reference to  FIG. 3  above. 
     It will be clear to a person skilled in the art that many variations of the above described auto-focus method can be used. For example, the relative order of the exposure and z-position steppings can be exchanged, i.e. a single exposure time can be followed by a series of z-position settings before the exposure time is updated to a new value. Method steps do not necessarily have to be executed from the shortest exposure time to the longest, or from the closest lens z-position to the farthest. The order can be inverted, or some other order can be used. 
     The present invention may be embodied in an automated image capturing systems, like, for instance, the Ariol Image Capturing System, which can be used to analyze a wide range of brightfield and fluorescent slides. The description below is given with reference to the epi-illuminated fluorescence microscope system, but the Ariol Image Capturing System can also be used with the trans-illumination fluorescence microscope system. The description of the image acquisition and analysis is given below with reference to  FIG. 6 , but many variations of the method are also possible in connection with the methods described in  FIGS. 3 and 5 . 
     At step  310  an assay is selected. This is a one-off configuration step where the stains, the objective/fluorescent filter combinations, the number of exposures, whether to blend the exposures or to keep them separate, and other configuration inputs are defined. The assay also defines how a slide is scanned and can include multiple passes, e.g. a pre-scan pass over the whole slide using an algorithm to automatically find tissue areas; a focus-only scan to build up a focus map; and a main-scan pass over the tissue areas found above, using focus map information from the focus-only pass. The main-scan captures the digital images of the tissue at different exposures, and blends them into a composite well contrasted image. 
     At step  320  the objective/filter combination is selected for the multi-exposure digital image capture. 
     At step  330  the user turns on the multi-exposures toggle button to tell the system to acquire multiple exposures at every slide or a sub-region of the slide. 
     At step  340  the user adds the required number of exposures by, for example, clicking on “add” button repeatedly until the required number of exposures is set. 
     At step  350  the images are acquired as per assay instructions. The images are now available for the analysis. 
     At step  360  the operator highlights each exposure in the exposure list and enters appropriate values per exposure. Inappropriate exposures, too high or too low, result in black or white saturation, both producing low weighting coefficients as the localized histogram variations will be small in either the black or the white saturation. Manually selected exposures are what the operator would expect to be the typical range of exposures that covers the expected intensity variation in the sample. 
     At step  370  operator turns on the “exposure-blending” toggle button. The images are now combined into a single well contrasted image as explained with reference to  FIG. 3  at steps  140  to  170 . 
     At step  380  the composite well contrasted image is available for viewing and analysis. 
     As will be understood by those skilled in the art, the present invention may be embodied in other specific forms without departing from the essential characteristics thereof. These other embodiments are intended to be included within the scope of the present invention, which is set forth in the following claims.