Abstract:
A color threshold analyzer includes a converter to convert signals representative of a pixel color value, e.g., red (R), green (G), and blue (B) signals, to signals representing hue (H), saturation (S), and intensity (I) values. Comparators are provided to compare the hue value to upper and lower hue reference values, the saturation value to a lower saturation reference value, and the intensity value to upper and lower intensity values. A switch may be provided to select a desired orientation (i.e., clockwise or counter-clockwise) on a polar hue scale to define the values between the upper ad lower hue reference values. A color identifier may output a signal indicating that the pixel color value corresponds to a desired color range in response to each of the H, S, and I values falling within their associated range.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims priority to U.S. Provisional Patent Application Serial No. 60/147,374, filed Aug. 4, 1999. 
    
    
     BACKGROUND 
     The invention relates to the application of thresholds in light microscopy. 
     Light microscopy images may be automatically scanned to detect the presence of certain classes of objects, which may be distinguished from other classes of objects by unique characteristics, including color. Automatic scanning of an image for the presence of objects belonging to a particular class must allow for small variations in the characteristic color of the objects belonging to the class. Because real objects belonging to the class have a range of colors, rather than a single uniform characteristic color, they occupy a volume, rather than a single point, in a three-dimensional (3D) color space. Therefore, color ranges in the 3D color space may be defined, and if automatic scanning of an image detects colors within the range, the detection of an object belonging to the class may be registered. 
     The detection of a real class of objects by its color requires differentiating its characteristic color volume from color volumes of other objects. The characteristic color volume of a class of objects may be defined by digitizing the 3D color volume in color space. An object belonging to the class of object can then be detected by comparing the color of the object to a lookup table storing the digital representation of the 3D color volume. If each of the three colors defining the 3D color space is digitized to n bits, however, the lookup table of size n 3  bits would be required to determine whether an object were in the predefined object class or not, and large lookup tables hinder the speed at which the image can be scanned. 
     Alternatively, the color volume of a class of objects can be defined by minimum and maximum color thresholds for each of the three color coordinates of a 3D color space, i.e. by defining a rectilinear color volume that includes the smaller arbitrarily shaped color volume characteristic of the object class. An object belong to the class may then be detected by comparing its color to the minimum and maximum thresholds that define the rectilinear color volume. Using minimum and maximum color thresholds to define a color volume requires the storage of only six parameter values, which permits faster scanning of images. Since the rectilinear color volume is larger than the actual characteristic color volume of the class of objects, however, objects that do not belong to the class of objects, but whose color is similar to the characteristic color of the class, may be falsely detected as members of the class. 
     SUMMARY 
     A color threshold analyzer includes a converter to convert signals representative of a pixel color value, e.g., red (R), green (G), and blue (B) signals, to signals representing hue (H), saturation (S), and intensity (I) values. Comparators are provided to compare the hue value to upper and lower hue reference values, the saturation value to a lower saturation reference value, and the intensity value to upper and lower intensity values. A switch may be provided to select a desired orientation (i.e., clockwise or counter-clockwise) on a polar hue scale to define the values between the upper ad lower hue reference values. 
     A color identifier may output a signal indicating that the pixel color value corresponds to a desired color range in response to each of the H, S, and I values falling within their associated ranges. 
     The reference values may be analog or digital signals. Digital reference signals may be stored in registers associated with the comparators. Analog reference signals may be supplied to the comparators by digital-to-analog signals set from the bus of a host computer. 
     The color threshold analyzer may be implemented in hardware, software, or a combination of both. 
    
    
     DESCRIPTION OF DRAWINGS 
     FIG. 1 is a schematic diagram of the imaging of a sample by a microscope. 
     FIG. 2 is a view of three-dimensional RGB color coordinate system. 
     FIG. 3 is view of a threshold region bounding a color subvolume in RGB color space. 
     FIG. 4 is view of a two-dimensional projection of a three-dimensional color subvolume in RGB color space. 
     FIG. 5 is a schematic diagram of a circuit. 
     FIG. 6 is a view of a two-dimensional projection of a three-dimensional color subvolume in HSI color space. 
     FIG. 7 is a schematic diagram of a machine vision system according to an embodiment. 
     Like reference symbols in the various drawings indicate like elements. 
    
    
     DETAILED DESCRIPTION 
     Referring to FIG. 1, a sample  100  containing multiple objects of different types is viewed with a microscope  110 , which creates an image  120  of the objects. Sample  100  may be a slide prepared with multiple cells of different types, and the cells may be stained with a dye to highlight contrasts in color between different types of cells in sample  100 . Image  120  is digitized by digitizer  130 , which may be internal to microscope  110 , if microscope  110  is a digital microscope, or which maybe be external to microscope  110 , if microscope  110  produces analog images of sample  100 . Digitizer  130  produces a digital color image  140  of sample  100 . 
     Digital color image  140  is composed of numerous pixels, which each have colors associated with them. Each pixel of digital color image  140  is analyzed by color analyzer  150 , which measures the color content of the pixels at three different colors. Any three distinguishable colors may be used, but red, green, and blue are the colors most often sampled by color analyzer  150 . Color analyzer  150  produces signals corresponding to the amount of color present in a pixel of digital color image  140  at each of the three distinguishable colors. Once the pixels of digital color image  140  have been analyzed for color by color analyzer  150 , the detection of objects of different types may be performed by comparing the color signals to a characteristic 3D color volume of a particular type of object. 
     Referring to FIG. 2, a three dimensional (3D) color coordinate space  10  may be defined, in which black is assigned to the origin  12 , and a red axis  13 , a blue axis  14  and a green axis  15  define coordinates of the space. The characteristic color of a common class of objects may be represented by a color subvolume  30  within color space  10 . If a type of object has a characteristic color, the profile of subvolume  30  may be built by examining the color of a number of objects of that type. For example, a microscope slide sample  100  with cancer cells, normal cells and background regions may be prepared and imaged. The pixels of the digital color image  140  of sample  100  maybe be color analyzed and plotted in a 3D color coordinate space, to reveal three color subvolumes, characteristic of the cancer cells, healthy cells and background. 
     Referring to FIG. 3, a characteristic subvolume  30  may be bounded by a larger rectilinear volume  33  defined by six parameters that represent the minima and maxima of the red, green, and blue color coordinate values of subvolume  30 . Rectilinear volume  33  can be defined by far fewer parameters than would be needed to define arbitrarily shaped characteristic color subvolume  30 . 
     Referring to FIG. 4, two dimensional (2D) projections  20  of 3D subvolumes  30  may be displayed in a 2D color coordinate space  21 . Projections  20  of characteristic color subvolumes  30  frequently are elongated along an axis between white and a saturated color in light microscopy. The elongation arises when a sample of objects, e.g. cells, varies in thickness or in dye uptake. The lighter or thinner portions appear mixed with white light, and the denser or thicker portions appear more saturated in color. 
     Two dimensional (2D) projections  20  of distinguishable 3D subvolumes  30  may be used to distinguish different types of objects. For example, an ovoid shaped projection of a characteristic subvolume of cancer cells  22  may be distinguishable from an ovoid shaped projection of a characteristic subvolume of healthy cells  26 , i.e. ovoid projections  22 ,  26  do not overlap in 2D color space  21 . If the characteristic color subvolumes  30  of cancer cells and healthy cells are parameterized by minimum and maximum color values and the resulting rectilinear subvolumes  33  are projected onto the 2D color space, however, their resulting rectangular projections  23 ,  27  may overlap. If rectangular projections  23 ,  27  are used to detect different types of objects, false detections may result because a point in region  24  that should be identified as characteristic of a cancer cell could be confused with a healthy cell since it falls in region  27  as well as in region  23 . Similarly, a point in region  28  that should be identified as characteristic of a healthy cell could be confused with a cancer cell because it falls in region  23  as well as region  27 . This is due both to the similar colors of cancer and healthy cells and the elongation of two dimensional (2D) projections  20  of 3D subvolumes  30 . 
     Characteristic 3D subvolumes  30  may, however, be distinguished using minimum and maximum color values to define a rectilinear region if a coordinate transformation of 3D color coordinate space  10 , and all characteristic subvolumes  30  within it, is first performed. Referring to FIG. 5, a circuit  200  may receive three color signal inputs  202 , which may be red, green, and blue, for each pixel of digital color image  140 . Color signal inputs  202  are received by a converter  210  that converts the representation of a pixel&#39;s color from red, green, and blue (RGB) signals  202  to hue, saturation, and intensity signals (HSI)  220 . The conversion of RGB signals  202  to HSI signals  220  is equivalent to a transformation from the rectilinear RGB coordinate system used in color space  10 , to a cylindrical coordinate system in which hue is the polar coordinate, saturation is the radial coordinate, and intensity is the axial coordinate, whose axis lies on a line between black and white in coordinate space  10 . A number of algorithms to perform this conversion are known, and computer chips are available to perform the algorithms. 
     Converter  210  generates HSI signals  220 , including a hue, signal  221 , a saturation signal  222 , and an intensity signal  223 . HSI signals  220  can be used to represent the characteristic color of a type of object in sample  100  in an HSI coordinate space. Referring to FIG. 6, 2D projections  230  of characteristic color 3D subvolumes of several types of objects may be displayed in a 2D hue-saturation (HS) coordinate space  225 , where hue is the polar coordinate and saturation is the radial coordinate. For example, the 2D projection of the characteristic color of cancer cells  231  in sample  100  may have a yellow hue; the 2D projection of the characteristic color of healthy cells  232  may have a green hue; and the 2D projection of the characteristic color of background regions  233  may have a blue-cyan hue. The characteristic colors of the three different types of objects are distinguishable in HS space  225 , as they are in RG space  21 . In HS space  225 , unlike RG space  21 , however, minimum and maximum coordinate values may be defined that bound the characteristic color subvolumes of a type of object and which define distinguishable regions in HS space  225 . Such minimum and maximum coordinate values may then be used to-detect the presence of certain types of objects without false attribution of objects. For example, a region  241  that bounds the characteristic subvolume of cancer cells  231  may be defined by a minimum saturation value, a minimum hue angle, and a maximum hue angle. Region  241  is distinguishable from region  252  that completely bounds the characteristic subvolume of healthy cells  232 , and which is defined by a different minimum saturation value, and different minimum and maximum hue angles. The axial intensity axis is suppressed for clarity purposes in FIG. 6, but the 3D regions defined by minimum and maximum hue, minimum and maximum saturation, and minimum and maximum intensity values characteristic of subvolumes of cancer cells and healthy cells do not overlap in the 3D HSI space either. 
     Further referring to FIG. 5, HSI signals  220  representing the color of a pixel may be used to determine if the pixel is part of the image of an object belonging to a particular class of objects, and thereby detect the presence of that type of object in sample  100 . After viewing many objects of a particular type under similar conditions, a characteristic color subvolume in HSI space may be constructed and minimum and maximum thresholds values of hue, saturation, and intensity may be defined that bound the characteristic color subvolume. The threshold values may then be used in a circuit  200 , to determine if a pixel belong to an object of a particular class of objects defined by the threshold values. In circuit  200  a hue maximum reference value  260 , a hue minimum reference value  261 , a saturation minimum reference value  262 , an intensity maximum reference value  263 , and an intensity minimum reference value  264  may be defined and stored as reference signals. The hue signal  221 , saturation signal  222 , and intensity signal  223  characterizing the color of the pixel may be compared to the respective reference signals using comparators  270 - 274 . Comparators  270 - 274  have an input A and an input B and produce a TRUE output if their A input is greater than their B input. The settable hue in/out bit  280  and the XOR gate  282  are necessary because hue is a polar coordinate and the predefined hue range can stretch from a higher hue number through zero to a lower number. If the in/out bit  280  is set HIGH, the output of XOR gate  282  is TRUE if the hue of the pixel is above the hue minimum reference value  261  and below hue maximum reference value  262 . If the in/out bit  280  is set LOW, the output of XOR gate  282  is TRUE if the hue of the pixel is below the hue minimum reference value  261  and above the hue maximum reference value  262 . AND gate  284  produces a TRUE signal if HSI signals  221 - 223  corresponding to the pixel&#39;s color are within the range defined by reference signals  260 - 264 . A TRUE signal from AND gate  284  indicates that the color of the pixel lies within the characteristic subvolume of the type of object to be detected. HSI signals  221 - 223  and references  260 - 264  may be digital or analog. Reference signals  260 - 264  may be set in a number of ways, for example, by setting digital latches or by a digital-to-analog signal set from the bus of a computer. 
     FIG. 7 illustrates a machine vision system  700  according to an embodiment that includes the circuit  200  shown in FIG.  5 . The machine vision system may be used to inspect objects based on color in an industrial application. A camera (or electronic microscope)  702 , which may produce analog or digital signals, scans the images of objects  704  on a background  706 . The output of the camera may be digitized (if analog) by an analog-to-digital converter (ADC)  708 . The objects may be stationary or moved under the camera  702  by a conveyor system. The reference signals  260 - 264  may be set by a host computer  712 . The circuit  200  may be connected to a dot clock  114 . The circuit  200  may analyze pixels at a dot clock rate for real time analysis of the objects. 
     According to an embodiment, the RGB and HIS color values are eight bit words. According to alternate embodiments, the system may operate on more or less than eight bit color values. According to these embodiments, the comparators, registers, and logic units have a depth large enough to accommodate the size of the color values. 
     A machine vision system according to an embodiment may be used for a variety of industrial and medical applications including, for example, inspecting colored components in a work piece, produce, color-coded pills, textiles, and stained cells. 
     The techniques described here may be implemented in hardware or software, or a combination of the two. The techniques may be implemented in computer programs executed on one or more programmable computers that may each includes a processor, a storage medium readable by the processor (including volatile and non-volatile memory and/or storage elements), and suitable input and output devices. The programmable computers may be either general-purpose computers or special-purpose, embedded systems. 
     A number of embodiments of the invention have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of the invention. Accordingly, other embodiments are within the scope of the following claims.