Abstract:
A method for segmenting spectrally-resolved images. The first step comprises acquisition of three images of the same micrographic scene. Each image is obtained using a different narrow band-pass optical filter which has the effect of selecting a narrow band of optical wavelengths associated with distinguishing absorption peaks in the stain spectra. The choice of optical wavelength bands is guided by the degree of separation afforded by these peaks when used to distinguish the different types of cellular material on the slide surface. By combining these images in a particular fashion, it is possible to achieve a high degree of success in separating the cervical cell from the background and the nuclei from the cytoplasm.

Description:
FIELD OF THE INVENTION  
         [0001]    The present invention relates to automated diagnostic systems, and more particularly to a system for multi-spectral segmentation for analyzing microscopic images.  
         BACKGROUND OF THE INVENTION  
         [0002]    Automated diagnostic systems in medicine and biology often rely on the visual inspection of microscopic images. Known systems attempt to mimic or imitate the procedures employed by humans. An appropriate example of this type of system is an automated instrument designed to assist a cytotechnologist in the review diagnosis of Pap smears. In its usual operation such a system will rapidly acquire microscopic images of the cellular content of the Pap smears and then subject them to a battery of image analysis procedures. The goal of these procedures is the identification of images that are likely to contain unusual or potentially abnormal cervical cells.  
           [0003]    The image analysis techniques utilized by these automated instruments are similar to the procedures consciously, and often unconsciously, performed by the human cytotechnologist. There are three distinct operations that must follow each other for this type of evaluation: (1) segmentation; (2) feature extraction; and (3) classification.  
           [0004]    The segmentation is the delineation of the objects of interest within the micrographic image. In addition to the cervical cells required for an analysis there is a wide range of “background” material, debris and contamination that interferes with the identification of the cervical cells and therefore must be delineated. Also for each cervical cell, it is necessary to delineate the nucleus with the cytoplasm.  
           [0005]    The Feature Extraction operation is performed after the completion of the segmentation operation. Feature extraction comprises characterizing the segmented regions as a series of descriptors based on the morphological, textural, densitometric and calorimetric attributes of these regions.  
           [0006]    The Classification step is the final step in the image analysis. The features extracted in the previous stage are used in some type of discriminant-based classification procedure. The results of this classification are then translated into a “diagnosis” of the cells in the image.  
           [0007]    Of the three stages outlined above, segmentation is the most crucial and the most difficult. This is particularly true for the types of images typically encountered in medical or biological specimens.  
           [0008]    In the case of a Pap smear, the goal of segmentation is to accurately delineate the cervical cells and their nuclei. The situation is complicated not only by the variety of cells found in the smear, but also by the alterations in morphology produced by the sample preparation technique and by the quantity of debris associated with these specimens. Furthermore, during preparation it is difficult to control the way cervical cells are deposited on the surface of the slide which as a result leads to a large amount of cell overlap and distortion.  
           [0009]    Under these circumstances segmentation operation is difficult. One known way to improve the accuracy and speed of segmentation for these types of images involves exploiting the differential staining procedure associated with all Pap smears. According to the Papanicolaou protocol the nuclei are stained dark blue while the cytoplasm is stained anything from a blue-green to an orange-pink. The Papanicolaou Stain is a combination of several stains or dyes together with a specific protocol designed to emphasize and delineate cellular structures of importance for pathological analysis. The stains or dyes included in the Papanicolaou Stain are Haematoxylin, Orange G and Eosin Azure (a mixture of two acid dyes, Eosin Y and Light Green SF Yellowish, together with Bismark Brown). Each stain component is sensitive to or binds selectively to a particular cell structure or material. Haematoxylin binds to the nuclear material colouring it dark blue. Orange G is an indicator of keratin protein content. Eosin Y stains nucleoli, red blood cells and mature squamous epithelial cells. Light Green SF yellowish acid stains metabolically active epithelial cells. Bismark Brown stains vegetable material and cellulose.  
           [0010]    The combination of these stains and their diagnostic interpretation has evolved into a stable medical protocol which predates the advent of computer-aided imaging instruments. Consequently, the dyes present a complex pattern of spectral properties to standard image analysis procedures. Specifically, a simple spectral decomposition based on the optical behaviour of the dyes is not sufficient on its own to reliably distinguish the cellular components within an image. The overlap of the spectral response of the dyes is too large for this type of straight-forward segmentation.  
         BRIEF SUMMARY OF THE INVENTION  
         [0011]    It has been found that although the stains according to the Papanicolaou protocol have evolved principally for the benefit of the cytotechnologist, computerized segmentation algorithms can employ this protocol to good effect if handled properly.  
           [0012]    The present invention provides a multi-spectral segmentation method particularly suited for Papanicolaou-stained gynaecological smears. The multi-spectral segmentation method is suitable for use in the automated diagnosis and evaluation of Pap smears.  
           [0013]    Micro-spectrophotometric investigation of Papanicolaou-stained cellular samples has established that there is a series of narrow spectral wavelength bands that can maximize the contrast between the three principal cellular components of the epithelial cell images; the nucleus, the cytoplasm and the background. At 570 nm the nuclei display maximum contrast against the cytoplasm. At 530 nm and 630 nm both varieties of cytoplasm are individually found to have maximal contrast against the image background.  
           [0014]    The method according to the present invention uses these three optical wavelength bands to segment the Papanicolaou-stained epithelial cells in digitized images. In a preferred embodiment, the present invention comprises a combination of a specialized imaging procedure and an executable algorithm. The method includes standard segmentation operations, for example erosion, dilation, etc., together with a careful linear discriminant analysis in order to identify the location of cellular components.  
           [0015]    The first step according to the method comprises the acquisition of three images of the same micrographic scene. Each image is obtained using a different narrow band-pass optical filter which has the effect of selecting a narrow band of optical wavelengths associated with distinguishing absorbtion peaks in the stain spectra. The choice of optical wavelength bands is guided by the degree of separation afforded by these peaks when used to distinguish the different types of cellular material on the slide surface. By combining these images in a particular fashion, it is possible to achieve a high degree of success in separating the cervical cell from the background and the nuclei from the cytoplasm.  
           [0016]    In a first aspect, the present invention provides a method for segmenting spectrally-resolved images, said method comprising the steps of: (a) forming an absorption image from each of said spectrally-resolved images; (b) generating absorption ratio images by forming ratios from selected pairs of said absorption images; (c) applying a linear discriminant analysis to said absorption ratio images to produce one or more segmentation output maps.  
           [0017]    In a second aspect, the present invention provides a system for segmenting spectrally-resolved images, said system comprising: (a) input means for inputting a plurality of spectrally-resolved images; (b) means for forming an absorption image from each of said spectrally-resolved images; (c) means for generating absorption ratio images by forming ratios from selected pairs of said absorption images; (d) linear discriminant analysis means for analyzing said absorption ratio images to produce one or more segmentation output maps.  
           [0018]    A preferred embodiment of the present invention will now be described by way of example, with reference to the following specification, claims and drawings.  
       
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0019]    [0019]FIG. 1 is a block diagram of a multi-spectral segmentation method according to the present invention;  
         [0020]    [0020]FIG. 2 is a block diagram showing production of absorbtion maps for FIG. 1;  
         [0021]    [0021]FIG. 3 is a block diagram showing production of absorbtion ratio maps for FIG. 1;  
         [0022]    [0022]FIG. 4 is a graphical representation of linear discriminant analysis according to the present invention; and  
         [0023]    [0023]FIGS. 5 i - 5   v  show in flow chart form a multi-spectral segmentation method according to the present invention. 
     
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT  
       [0024]    Reference is first made to FIG. 1 which depicts a multi-spectral segmentation method  10  according to the present invention. Preferably, the multi-spectral segmentation method  10  comprises a routine which is suitable for hardware-encoding, i.e. embedded in logic (e.g. Field Programmable Gate Array or FPGA logic) for a special-purpose computer. A suitable hardware architecture is described in applicant&#39;s co-pending international patent application entitled an IMAGE PREPROCESSOR FOR IMAGE ANALYSIS and filed simultaneously herewith.  
         [0025]    Referring to FIG. 1, the multi-spectral segmentation method  10  operates on three spectrally resolved images I 1 ,  12 , I 3 . The images comprise digitized scans of cellular specimens and preferably are generated by a digitizing camera of known design. It has been found that for Papanicolaou-stained cellular samples there is a series of narrow spectral wavelength bands which enhance the contrast between the three principal cellular components of the epithelial cell images: the nucleus, the cytoplasm and the background. The first image I 1  is scanned at 570 nanometres (nm) in order to enhance the contrast of the cytoplasm against the image background. The second image I 2  is scanned at 570 nm in order to enhance the contrast of the nuclei against the cytoplasm. Similarly, the third image I 3  is scanned at 630 nm to enhance the contrast between the cytoplasm and the image background. It will be understood that the Papanicolaou staining protocol produces two stained cytoplasms which are of interest.  
         [0026]    As shown in FIG. 1, the multi-spectral segmentation method  10  comprises three principal steps or operations  12 ,  14 ,  16  that are applied to images in order to produce a segmentation decision denoted by  18 . (The principal function of the multi-spectral segmentation routine is to delineate objects of interest within the digitized images of the cellular specimens.) Referring to FIG. 1, the first step  12  comprises processing the spectrally resolved images I 1 , I 2 , I 3  to produce a series of absorbtion maps AM 1 , AM 2 , AM 3 , respectively. The second step  14  involves combining the three absorbtion maps AM 1 , AM 2 , AM 3  (produced in step  12 ) to generate three absorbtion ratio maps ARM 1 , ARM 2 , ARM 3 . The third step  16  in the multi-spectral segmentation method  10  involves performing a four-dimensional linear discriminant analysis utilizing the three absorbtion ratio maps ARM 1 , ARM 2 , ARM 3  and one of the absorbtion maps, e.g. AM 2  as shown in FIG. 1.  
         [0027]    The first step  12  for producing the absorption maps AM 1 , AM 2  and AM 3  is depicted in FIG. 2. The operation in this step  12  relies on the observation that the light intensity images I 1 , I 2 , I 3  generated by the digital camera must follow the known Lambert&#39;s Law of optical absorbtion so that the intercepted light intensity is given by the following expression:  
           I=I   o   exp ( −αx )  (1)  
         [0028]    In expression (1), the parameter I is the intercepted light intensity, I o  is the incident intensity, a is the characteristic absorbtion coefficient of the material and x is its thickness. By taking the logarithm of each of the three images I 1 , I 2  and I 3 , absorption maps AM 1 , AM 2  and AM 3  are produced that are proportional to x as shown in FIG. 2 and given by the following expression:  
           In ( I ) =In ( I   o ) αx   (2)  
         [0029]    Referring to FIG. 2, the absorption maps AM 1 , AM 2 , AM 3  are produced from the application of expression (2) to the spectrally resolved images I 1 , I 2  and I 3  in block  12 .  
         [0030]    As described with reference to FIG. 1, the three absorption maps AM 1 , AM 2 , AM 3  are combined to produce three absorption ratio maps ARM 1 , ARM 2 , ARM 3 . The operation  14  for producing the absorption ratio maps ARM 1 , ARM 2 , ARM 3  is shown in more detail in FIG. 3 and involves applying the following scaling relation:  
               Ratio                 Map     =     arctan          In        (   1   )         In        (   2   )                   (   3   )                               
 
         [0031]    The absorption ratio maps ARM 1 , ARM 2 , ARM 3  produced through expression (3) have the advantage of being independent of the local thickness of the biological material. As shown in FIG. 3, the first ratio map ARM 1  is derived from the first and second absorption maps AM 1  and AM 2 , the second ratio map ARM 2  is derived from the first and third absorption maps AM 1  and AM 3 , and the third ratio map ARM 3  is derived from the second and third absorption maps AM 2  and AM 3 .  
         [0032]    As described above, the third step comprises applying a four-dimensional linear discriminant analysis to the three absorbtion ratio maps ARM 1 , ARM 2  and ARM 3  and one of the absorbtion maps AM 2 . The purpose of this step is to provide the optimal classification of cellular material based on absorbtion characteristics alone. An example of the two-dimensional counterpart for this type of analysis is illustrated in FIG. 4. For the two-dimensional analysis, the two characteristic measures, i.e. FEATURE A and FEATURE B, are enough to provide a proper discrimination between two types of material.  
         [0033]    According to this aspect of the invention, linear discriminant analysis for the segmentation of cytoplasm comprises four dimensions as follows: (1) arctan (In (1)/In(2)); (2) arctan (In(3)/In(2)); (3) arctan (In(3)/In(1)); and (4) In(2). The result of the linear discriminant analysis is the delineation between the nuclei and the cytoplasm. In the present instance, the linear discriminant analysis is designed to delineate between the nuclear material, the first cytoplasm material and the second cytoplasm material as defined according to the Papanicolaou staining protocol.  
         [0034]    Reference is next made to FIG. 5 which shows in more detail the Multi-Spectral Segmentation method or routine  10  according to the present invention. The principal function of the segmentation method  10  is the delineation of the objects of interest within the micrographic images, in this instance, nuclear and cytoplasm material in cellular Pap smears.  
         [0035]    The first operation performed by the multi-spectral segmentation method  10  is a levelling operation  100 . The levelling operation  100  comprises an image processing procedure which removes any inhomogenities in the illumination of the cellular images I 1 , I 2 , and  13  received on Channels A, B, C, respectively, from the digitizing camera (not shown). The levelling operation  100  utilizes “background” images, i.e. those that do not contain any cellular material, in order to remove the inhomogenities. One skilled in the art will be familiar with the implementation of the levelling operation and therefore additional description for this operation is not needed.  
         [0036]    Next, the levelled images, i.e. I 1 , I 2  and I 3 , are processed by a logarithm module  102 . The logarithm module  102  corresponds to the absorption map generation step  12  described above with reference to FIGS. 1 and 2. The module  102  utilizes the natural logarithm function to produce the absorbtion maps. AM 1 , AM 2  and AM 3  from the levelled images I 1 , I 2  and I 3 .  
         [0037]    The multi-spectral segmentation routine  10  then calls a ratio module  104  which provides the absorption ratio map production operation described above. The ratio module  104  takes a logarithmic ratio of each of the two-image combinations, i.e. AM 1 /AM 2 , AM 2 /AM 3  and AM 1 /AM 3 , in order to eliminate the thickness-dependence of the absorbtion maps AM 1 , AM 2 , AM 3 . The output of the ratio module  104  is the absorption ratio maps ARM 1 , ARM 2  and ARM 3 .  
         [0038]    The next step in the segmentation routine  10  comprises the discriminator operation  106 . As described above, the routine  10  utilizes a four-dimensional linear discriminant analysis. The discriminator  106  comprises a module that uses the four absorbtion maps to identify the material in an image, i.e. discriminant between the nuclear material and the two types of cytoplasm material. The four inputs to the discriminator  106  are the three absorption ratio maps generated by module  104 :  
         [0039]    (1) arctan (In(I 1 )/In(I 2 ))  
         [0040]    (2) arctan (In(I 3 )/In(I 2 ))  
         [0041]    (3) arctan (In(I 3 )/In(I 1 ))  
         [0042]    and the fourth dimension is provided by the second absorption map AM 2  (i.e. In(I 2 )). As shown in FIG. 5 v , the output from the discriminator  106  is two binary images comprising a first cytoplasm ( 1 ) map  108  and a second cytoplasm ( 2 ) map  110 . The two cytoplasm maps  108 ,  110  correspond to the two types of cytoplasm material derived from the Papanicolaou staining protocol. Preferably, the discriminator  106  is implemented using a “look-up” table structure in which the pixels provide addressing into the table in order to look-up the identification of the material of interest, e.g. cytoplasm  1  material or cytoplasm  2  material. Knowing the four inputs to the discriminator module  106  as described above, the implementation of the discriminator  106  is within the understanding of one skilled in the art.  
         [0043]    As shown in FIGS. 5 i  and  5   v , the second absorption map AM 2  also provides an input to a threshold module  112 . The threshold module  112  applies a threshold to the second absorption map AM 2  which divides the absorption map AM 2  into regions that have a pixel value over a particular number (the threshold number) from those whose value is under the threshold number in order to delineate the nuclear material in the image map AM 2 . The output from the threshold module  112  is a 1st nuclear map  114 . The 1st nuclear map  114  comprises a binary (two-level) image and is used in further identification operations as will be described below.  
         [0044]    Referring to FIG. 5 v , the first and second cytoplasm maps  108 ,  110  provide the inputs to an OR module  116 . The function of the OR module  116  is to logically OR the binary image inputs, i.e. cytoplasm maps  108 ,  110 . The logic OR operation produces an output binary image comprising the logical OR of the two cytoplasm maps  108 ,  110  and designated a 1st cytoplasm map  118 .  
         [0045]    As shown in FIG. 5 v , the 1st cytoplasm map  118  provides an input to a module  120 . The other input for the module  120  is the 1st nuclear map  114  which was generated by the threshold module  112 . The module  120  compares the 1st Nuclear map  114  with the 1st cytoplasm map 118 in order to eliminate areas in the 1st nuclear map  114  that are dark cytoplasm. The output from the module  120  is a 2nd nuclear map  122 .  
         [0046]    The 2nd nuclear map  122  provides the input to an erode module  124 . The module  124  performs an erosion operation on the 2nd nuclear map  122 . The erosion operation comprises a standard image processing operation and is typically applied to binary images or maps. The erosion operation applies a rule to determine whether a particular pixel in the binary image should be “ON” or “OFF”, that is, take the value of zero or one. In the case of erosion, the pixels of interest in the binary image are ON, and the determination is whether the pixel remains ON or is turned OFF. This determination is based on the binary state of the adjacent pixels, as will be understood by one skilled in the art. The erosion operation is used to “clean-up” the segmentation results by quickly extinguishing small random pixels that have inadvertently been identified as nuclei, etc. The binary image output from the erosion module  124  provides one input to a remove peak areas module  126 . The other input for the module  126  is derived from the levelled image I 2  (FIG. 5 i ).  
         [0047]    As shown in FIGS. 5 i  and  5   ii , the levelled image I 2  also goes to a Sobel filter module  128 . The Sobel filter  128  performs a standard gradient filter technique. The output from the Sobel filter  128  goes to a peak location module  130 . The function of the peak location module  130  is to locate the highest values of the pixels in the filtered image I 2 ′. The output from the peak location module  130  provides the other input to the remove peak areas module  126 . The remove peak areas module  126  compares the 2nd nuclear map  122  with the peaks in the Sobel map in order to remove small and dark debris.  
         [0048]    Referring back to FIG. 5 ii , the output from the Sobel filter module  128  also goes to a threshold module  132 . The threshold module  132  applies a threshold in order to divide the Sobel map image (i.e. output from Sobel filter  128 ) into regions that have a pixel value between a lower and upper threshold and those that do not fall within this range of values, typically fixed between 32 and 200. The output from the threshold module  132  goes to an erosion and dilation operations module  134 . The erosion and dilation operations are standard image processing techniques, and the erosion operation is described above. The dilation operation is similar to the erosion operation except that the rule is inverted to apply to “OFF” pixels and the number of adjacent “ON” pixels. The effect of the dilation operation is to gradually increase the size of the “ON” regions in a binary image as will be apparent to one skilled in the art. The output from the erosion and dilation module  134  is an edge map image  136  of the image I 2 .  
         [0049]    Referring to FIG. 5 v , the edge map  136  provides one input to a special dilation (1) module  138 . The other input for the special dilation (1) module  138  is the output from the remove peak areas module  126  (i.e. the 2nd nuclear map  122  with the small and dark debris removed). The special dilation (1) module  138  performs a dilation operation that employs the rule that the dilated regions will not go outside the boundaries of the edge map  136 . In known manner, the dilation operation “expands” a region of interest in a digital image as described above. The result of the special dilation (1) module  138  is a 3rd nuclear map denoted by reference  140  in FIG. 5 iv.    
         [0050]    Referring to FIG. 5 iv , the 3rd nuclear map  140  goes to an erode twice module  142 . The erode module  142  in known manner twice performs the erosion operation on the nuclear map  140 . The twice eroded nuclear map then goes to a label objects module  144 . The label objects module  144  attaches a unique numeric label to all of the pixels that form a distinct region (i.e. within a boundary) in the twice eroded nuclear map. In this instance, the distinct regions of interest comprise nuclei and the label objects module  144  assigns a unique identifier to each nuclear region in the nuclear map. This allows each distinct region, i.e. nuclei, in the nuclear map to be identified in subsequent operations. It will be appreciated that as operations are performed on labelled regions those regions may gain or lose pixels.  
         [0051]    As shown in FIG. 5 iv , the output from the label objects module  146  goes to a special dilation (2) module  146 . The other input to the special dilation (2) module  146  is provided by the 3rd nuclear map  140 . The special dilation (2) module  146  performs a dilation operation and employs the rule that the dilated regions will not go outside the 3rd nuclear map  140 . The result for the special dilation (2) module  146  is a final nuclear image map  148 .  
         [0052]    As shown in FIG. 5 iv , the multi-spectral segmentation routine  10  includes another special dilation (3) module  150  which applies a dilation operation to the final nuclear map  148  and a final cytoplasm map  152  to generate a final surround map  154 . The special dilation (3) module  150  performs a dilation operation that employs the rule that the dilated regions will not go outside the final cytoplasm map  152 . The final surround map  154  comprises a map in which each nuclei is associated with a portion of the cytoplasm.  
         [0053]    Referring to FIG. 5 iii , the final cytoplasm map  152  is generated from the 1st cytoplasm map  118  (FIG. 5 v ). The 1st cytoplasm map  118  is processed by an erosion module  156  and a special dilation (4) module  158 . The special dilation (4) module  158  performs a dilation operation that employs the rule that the dilated regions will not go outside the 1st cytoplasm map  118 . The result of the erosion module  156  is to gradually reduce size and regularize the shape of the cytoplasm regions of the 1st cytoplasm map  118 , while the result of the dilation module  158  is to gradually increase the size of the cytoplasm regions in the 1st cytoplasm map  118 . By applying the erosion operation a few times, small and unimportant regions are effectively removed from the binary map. The dilation operation is then applied successively to “re-grow” the remaining regions in the binary image back to their former dimensions.  
         [0054]    The output from the dilation module  158  is a 2nd cytoplasm map  160 . Next, the 2nd cytoplasm map  160  is logically OR&#39;d with the 3rd nuclear map  140  (FIG. 5 iv ) by a logical OR module  162 . The output from the OR module  162  is then applied to a label objects module  164 . The label objects module  164  for the cytoplasm map attaches a unique numeric label to all of the pixels that form a distinct region (i.e. within a boundary) in the cytoplasm map. In the present instance, distinct regions of interest comprise cytoplasm material. This allows each distinct region in the cytoplasm map to be identified in subsequent operations. The special dilation (5) module  166  performs a dilation operation that employs the rule that the dilated regions will not go outside the 2nd cytoplasm map  160 . The output from the special dilation (5) module  166  is the final cytoplasm map  152 .  
         [0055]    The final surround map  154  (and final cytoplasm map  152  and final nuclear map  148 ) produced by the multi-spectral segmentation process  10  are available for further processing, i.e. feature extraction and classification, in order to identify unusual or potentially abnormal cellular structures or features.  
         [0056]    Summarizing, the multi-spectral segmentation method or routine according to the present invention has the following advantages. First, the method reduces the degree of error typically associated with the segmentation decisions by correlating a series of observations concerning the distribution pattern of material absorbtion. It is a feature of the present invention that the method is well-suited for a hardware-encoded implementation, for example using Field Programmable Gate Array(s). Field Programmable Gate Arrays (FPGA&#39;s) comprise integrated circuit devices that are programmable and provide execution speeds that approach the levels of speed expected from a dedicated or custom silicon device. A hardware-encoded implementation enables the routine to operate at maximum speed in making the complex decisions required. Secondly, the-method is applicable to a multiplicity of similar types of discriminant analysis. For example as further experimental data is tabulated and evaluated more complex discriminant hyper-surfaces can be defined in order to improve segmentation accuracy. Accordingly, the description of the decision hyper-surface can be modified through the adjustment of a table of coefficients.  
         [0057]    It is therefore to be understood that the foregoing description of the preferred embodiment of this invention is not intended to be limiting or restricting, and that various rearrangements and modifications which may become apparent to those skilled in the art may be resorted to without departing from the scope of the invention as defined in the claims.