Method and apparatus for automatic digital image analysis

Claims

We claim:

1. A digital image processing system for detecting specific shapes, comprising:

(a) image gathering means for providing a series of electronic signals representing a series of visual images;

(b) conversion means for converting each of said electronic signals to a digital representation and for modifying said digital representation according to external commands;

(c) memory means for storing each said digital representation as an array of pixels, each of said pixels including a value representing gray shading for a unit of said visual image;

(d) processing means coupled to said memory means and to said conversion means for issuing said commands to said conversion means and for detecting and analyzing changes in successive said digital representations;

(e) processing means further comprising means for transforming each said digital representation by:

(i) dilating said digital representation by a series of first structuring elements to provide a dilated digital representation;

(ii) subtracting said dilated digital representation from said digital representation, for identification of high contrast portions of said visual image;

(iii) threshold comparison of said high contrast portions by comparing the gray value of each of said pixels of said high contrast portions with a first reference value, replacing the gray values for each of said pixels whose value exceeds or equals said first reference value with a first fixed gray value and replacing the gray values for each of said pixels whose value is less than said first reference value with a second fixed gray value to provide a first threshold image;

(iv) performing an annulus transformation on said first threshold image to create an annulustransformed image by:

(1) determining a numerical value for the degree of coincidence between pixels having said first fixed gray value in said first threshold image and masks having annular patterns of pixels, said numerical value representing a degree of ring closure, and

(2) storing in said memory means the numerical value for said degree of ring closure and coordinates corresponding to the center of each of said annular masks;

(v) performing a ring-toss transformation on said annulus-transformed image to create a ring-toss transformed image by:

(1) generating a pattern of pixels by dilation of said annulus-transformed image by a second annular structuring element, and

(2) subtracting said dilated annulustransformed image from said annulustransformed image;

(vi) generating a second threshold image by threshold comparison of said ring-toss transformed image by comparing the gray value of each of said pixels of said ring-toss transformed image with a second reference value, replacing the gray values for each of said pixel whose value exceeds or equals said second reference value with a third fixed gray value and replacing the gray values in each of said pixels whose value is less than said second reference value with a fourth fixed gray value;

(vii) storing said second threshold image in said memory means;

(f) means for comparing successive second threshold images of said transformed digital representations to detect appearances of said specific shapes.

2. The image processing system of claim 1 wherein said processing means comprises reading means for reading said digital representations from said memory.

3. The image processing system of claim 2 wherein said processing means comprises writing means for writing said digital representations to said memory.

4. The image processing system of claim 3 wherein said processing means includes logic means for generating structuring elements, said structuring elements including a specific pattern of bits.

5. The image processing system of claim 4 wherein said processing means comprises logic means for executing logic operations including the functions of logical AND, OR, NOT and exclusive-OR.

6. The image processing system of claim 5 further comprising mass storage means coupled to said processing means for permanent storage of data.

7. The image processing system of claim 6 wherein said processing means comprises logic means for creating lists of data in said memory.

8. The image processing system of claim 7 wherein said processing means comprises searching means for identifying particular elements in said lists of data.

9. The image processing system of claim 8 further comprising an input means coupled to said processing means whereby parameters supplied by a user may be stored in said memory.

10. The image processing system of claim 9 wherein said processing means comprises control means for issuing commands to said conversion means.

11. The image processing system of claim 10 wherein said control means comprises sequencing means for reading a series of instructions from said memory for transmission to said conversion means.

12. The image processing system of claim 11 wherein said processing means further comprises arithmetic means for execution of arithmetic operations including ADDITION, SUBTRACTION, MULTIPLICATION and DIVISION.

13. A method for identifying and recording the appearance of specific objects as digital representations of a series of visual images in a digital image processing system, said objects including patterns of pixels, comprising the steps of:

(a) converting each said visual image into a digital representation;

(b) providing memory means for storage of each said digital representation as an array of pixels, each pixel including a value representing gray shading for a unit of said visual image;

(c) transforming said digital representation by:

(i) dilating said digital representation by a series of first structuring elements to provide a dilated digital representation;

(ii) subtracting said dilated digital representation from said digital representation, for identification of high contrast portions of said visual image;

(iii) threshold comparison of said high contrast portions by comparing the gray value of each of said pixels of said high contrast portions with a first reference value, replacing the gray values for each of said pixels whose value exceeds or equals said first reference value with a first fixed gray value and replacing the gray values for each of said pixels whose value is less than said first reference value with a second fixed gray value to provide a first threshold image;

(iv) performing an annulus transformation on said first threshold image to create an annulustransformed image by:

(1) determining a numerical value for the degree of coincidence between pixels having said first fixed gray value in said first threshold image and masks having annular patterns of pixels, said numerical value representing a degree of ring closure, and

(2) storing in said memory means the numerical value for said degree of ring closure and coordinates corresponding to the center of each said annular masks;

(v) performing a ring-toss transformation on said annulus-transformed image to create a ring-toss transformed image by:

(1) generating a pattern of pixels by dilation of said annulus-transformed image by a second annular structuring element, and

(2) subtracting said dilated annulustransformed image from said annulustransformed image;

(vi) generating a second threshold image by threshold comparison of said ring-toss transformed image by comparing the gray value of each of said pixels of said ring-toss transformed image with a second reference value, replacing the gray values for each of said pixels whose value exceeds or equals said second reference value with a third fixed gray value and replacing the gray values in each of said pixels whose value is less than said second reference value with a fourth fixed gray value;

(vii) storing said second threshold image in said memory means;

(d) comparing successive said transformed digital representations to detect appearances of said objects;

(e) recording appearances of said objects; and

(f) displaying appearances of said objects.

14. The method as defined by claim 13 wherein said second threshold image is used to update said digital representation stored in said memory means, using a logical OR operation.

15. The method as defined by claim 14 further comprising the step of isolating significant objects in said digital representation by connectivity number.

16. The method as defined in claim 15 further comprising the step of creating a temporal vicinity list in said memory, said temporal vicinity list comprising:

coordinates of an identified object;

a degree of ring closure of said object;

a first time said object was first detected;

a second time said object was last detected; and

an index in said temporal vicinity list of another object to which said identified object is paired.

17. The method as defined in claim 16 further including the step of pairing said identified objects by storing the coordinates of each object in the pair in the temporal vicinity list entry of the other object in said pair.

18. The method as defined in claim 17 wherein said pairing is applied only to objects within a specific number of pixels of each other.

19. The method as defined in claim 18 wherein said pairing takes place between the identified object and the nearest other object when more than one object is within said specific number of pixels.

20. The method as defined in claim 19 further including the step of recording all incidences of said pairing on said mass storage means.

21. The method as defined in claim 20, further including the step of updating said temporal vicinity list for each of said objects identified in said binary representation.

22. The method as defined in claim 21 wherein said updating includes searching said temporal vicinity list for other objects within a specified proximity of said identified object, adding said identified object to the temporal vicinity list if no such other object is found and, if such other object is found, storing the coordinates and degree of ring closure information for said object in the node for said identified object in the temporal vicinity list.

23. The method as defined in claim 22 further including the step of scanning said temporal vicinity list for objects which have aged, such aging occurring when said object is last identified more than a specified time prior to the current visual image being processed.

24. The method as defined in claim 23 further including the step of deleting from said temporal vicinity list all of said objects which have aged and are not paired with another object.

25. The method as defined in claim 24 further including the step of deleting paired objects only when both objects in said pair have aged.

26. The method as defined in claim 25 further including the step of displaying for the use all incidences of said parings on said display means.

27. A digital image processing system as in claim 12, comprising means for recording appearances of said specific shapes.

28. A digital image processing system as in claim 27, comprising means for displaying appearances of said specific shapes.

29. A pattern recognition system for detecting annular objects in visual images, each of said visual images having been converted to a digital representation as an array of pixels stored in a memory means, each of said pixels having an associated numerical value representing gray shading, comprising: means for transforming each said digital representation by:

(a) dilating said digital representation by a series of first structuring elements to provide a dilated digital representation;

(b) subtracting said dilated digital representation from said digital representation, for identification of high contrast portions of said visual image;

(c) threshold comparison of said high contrast portions by comparing the gray value of each of said pixels of said high contrast portions with a first reference value, replacing the gray values for each of said pixels whose value exceeds or equals said first reference value with a first fixed gray value and replacing the gray values for each of said pixels whose value is less than said first reference value with a second fixed gray value to provide a first threshold image;

(d) performing an annulus transformation on said first threshold image to create an annulus-transformed image by:

(i) determining a numerical value for the degree of coincidence between pixels having said first fixed gray value in said first threshold image and masks having annular patterns of pixels, said numerical value representing a degree of ring closure, and

(ii) storing in said memory means the numerical value for said degree of ring closure and coordinates corresponding to the center of each of said annular masks;

(e) performing a ring-toss transformation on said annulus-transformed image to create a ring-toss transformed image by:

(i) generating a pattern of pixels by dilation of said annulus-transformed image by a second annular structuring element, and

(ii) subtracting said dilated annulus-transformed image from said annulus-transformed image;

(f) generating a second threshold image by threshold comparison of said ring-toss transformed image by comparing the gray value of each of said pixels of said ring-toss transformed image with a second reference value, replacing the gray values for each of said pixels whose value exceeds or equals said second reference value with a third fixed gray value and replacing the gray values in each of said pixels whose value is less than said second reference value with a fourth fixed gray value;

(g) storing said second threshold image in said memory means.

30. A pattern recognition system as in claim 29, comprising:

(a) means for comparison of successive stored second threshold images of said transformed digital representation to detect appearance of objects in said second threshold images including:

(i) means for updating said stored transformed digital representation using said second threshold image in a logical OR operation;

(ii) means for identifying objects by isolating significant objects in said transformed digital representation based upon connectivity number of said objects;

(iii) means for creating a temporal vicinity list in said memory, said temporal vicinity list comprising:

coordinates of an identified object,

a degree of ring closure of said object,

a first time said object was first detected,

a second time said object was last detected, and

an index in said temporal vicinity list of another object to which said identified object is paired;

(iv) means for pairing said identified objects by storing the coordinates of each object in the pair in the temporal vicinity list entry of the other object in said pair, wherein said pairing is applied only to objects within a specific number of pixels of each other, and wherein said pairing takes place between the identified object and the nearest other object when more than one object is within said specific number of pixels;

(v) means for recording all instances of said pairing in said storage means;

(vi) means for updating said temporal vicinity list for each of said objects identified in said transformed digital representation, wherein said updating includes searching said temporal vicinity list for other objects within a specified proximity of said identified object, adding said identified object to the temporal vicinity list if no such other object is found and, if such other object is found, storing the coordinates and degree of ring closure information for said other object in a node for said identified object in the temporal vicinity list;

(vii) means for scanning said temporal vicinity list for objects which have aged, such aging occurring when said object is last identified more than a specific time prior to the current visual image being processed;

(viii) means for deleting from said temporal vicinity list all of said objects which have aged and are not paired with another object;

(ix) means for deleting paired objects from said temporal vicinity list only when both objects in said pair have aged; and

(x) means for displaying all instances of said pairings.

31. A pattern recognition method for detecting annular objects in visual images, each of said visual images having been converted to a digital representation as an array of pixels stored in a memory, each of said pixels having an associated numerical value representing gray shading, comprising the steps of: transformation of said digital representation by:

(a) dilating said digital representation by a series of first structuring elements to provide a dilated digital representation;

(b) subtracting said dilated digital representation from said digital representation, for identification of high contrast portions of said visual image;

(c) threshold comparison of said high contrast portions by comparing the gray value of each of said pixels of said high contrast portions with a first reference value, replacing the gray values for each of said pixels whose value exceeds or equals said first reference value with a first fixed gray value and replacing the gray values for each of said pixels whose value is less than said first reference value with a second fixed gray value to provide a first threshold image;

(d) performing an annulus transformation on said first threshold image to create an annulus-transformed image by:

(i) determining a numerical value for the degree of coincidence between pixels having said first fixed gray value in said first threshold image and masks having annular patterns of pixels, said numerical value representing a degree of ring closure, and

(ii) storing in said memory the numerical value for said degree of ring closure and coordinates corresponding to the center of each of said annular masks;

(e) performing a ring-toss transformation on said annulus-transformed image to create a ring-toss transformed image by:

(i) generating a pattern of pixels by dilation of said annulus-transformed image by a second annular structuring element, and

(ii) subtracting said dilated annulus-transformed image from said annulus-transformed image;

(f) generating a second threshold image by threshold comparison of said ring-toss transformed image by comparing the gray value of each of said pixels of said ring-toss transformed image with a second reference value, replacing the gray values for each of said pixels whose value exceeds or equals said second reference value with a third fixed gray value and replacing the gray values in each of said pixels whose value is less than said second reference value with a fourth fixed gray value;

(g) storing said second threshold image in said memory.

32. A pattern recognition method as in claim 31, comprising the steps of:

(a) comparison of successive stored second threshold images of said transformed digital representation to detect the appearance of objects in said second threshold images;

(b) updating said stored transformed digital representation using said result of said transforming step in a logical OR operation;

(c) identifying objects by isolating significant objects in said transformed digital representation based upon connectivity number of said objects;

(d) creating a temporal vicinity list in said memory, said temporal vicinity list comprising:

(i) coordinates of an identified object,

(ii) a degree of ring closure of said object,

(iii) a first time said object was first detected,

(iv) a second time said object was last detected, and

(v) an index in said temporal vicinity list of another object to which said identified object is paired;

(e) pairing said identified objects by storing the coordinates of each object in the pair in the temporal vicinity list entry of the other object in said pair, wherein said pairing is applied only to objects within a specific number of pixels of each other, and wherein said pairing takes place between the identified object and the nearest other object when more than one object is within said specific number of pixels;

(f) recording all instances of said pairing in said memory;

(g) updating said temporal vicinity list for each of said objects identified in said transformed digital representation, wherein said updating includes searching said temporal vicinity list for other objects within a specified proximity of said identified object, adding said identified object to the temporal vicinity list if no such other object is found and, if such other object is found, storing the coordinates and degree of ring closure information for said other object in a node for said identified object in the temporal vicinity list;

(h) scanning said temporal vicinity list for objects which have aged, such aging occurring when said object is last identified more than a specific time prior to the current visual image being processed;

(i) deleting from said temporal vicinity list all of said objects which have aged and are not paired with another object;

(j) deleting paired objects from said temporal vicinity list only when both objects in said pair have aged; and

(k) displaying all instances of said pairings.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention relates generally to image processing and, more specifically, to automated digital image processors useful for the identification of dividing cells for the examination of various biological cellular events.

2. Prior Art

Much of the experimental work in the field of cell and molecular biology of cultured cells involves the assay of the proliferative activity of these cells. This is especially true for problems in biological time, rhythmicity and time sense which require for their resolution continuous surveillance and frequent data acquisition.

Critical to such work is an accurate count of incidents of mitosis, or cell division, in a cell culture. Incidents of mitosis are easily identified because mitotic cells in culture tend to round up and become refractile and annular in appearance under phase contrast illumination. Thus it is possible for a human observer to count incidents of mitosis by observing the cell culture under such illumination.

Up to the present time, all of this information has been collected by researchers by hand without the extensive use of computers. This is a particular problem because the need for extensive observation of the growth of cells in such cultures makes human observation an inconveniently long and tedious process in a laboratory setting. In addition, such observation requires a high degree of accuracy, and consequently several observers must be used to insure such accuracy.

One present method for performing such experiments is to place a plated culture of cells under a microscope under phase contrast illumination, and to record the microscopic images over time using a video tape recorder having time lapse videophotography capability. The time at which each cell divided, or more typically, elapsed time from a predetermined event can be determined from the videotapes. This information has been provided by a digital clock which generates a time signal that is superimposed on the viewing video screen. Thus, the number of cells dividing during the experiment, and the time at which each division occured can be obtained. A 48:1 time compression is generally used so that a 48 hour recorded observation can be viewed in one hour. Of course, since multiple events may occur on the screen at the same time, an observer may be required to review the tape a multiple number of times to observe all mitotic events. Thus, the review of a 48 hour tape may easily take eight hours or longer for a skilled human observer to complete.

What is required is an automated means for recording these incidents of mitosis which is rapid, automated, and non-perturbing. This last criteria is particularly important in research involving rhythmicity and time sense. As disclosed below, the present invention provides such a means. Such an automated system permits continual observation of cell cultures over extended periods of time that was not available under the manual methods of the prior art. The use of such automated system would make such data handling and analysis faster and significantly more accurate, and further would enable the researchers to derive additional information from such experiments which have heretofore been very difficult to obtain on a large population of cells, because of the difficulty in tracking individual cells and their progeny on a large scale.

SUMMARY OF THE INVENTION

The present invention provides methods and apparatus which are used in conjunction with a digital computer system to identify and record events in a binary representation of a visual image. Specifically, algorithm means are provided whereby incidents of mitosis in a cell culture can be identified by image analysis techniques.

Images are obtained using a video camera in combination with a microscope and low intensity phase contrast illumination to observe a cell culture. The signal from the video camera is then periodically sampled. The sampled signal is then digitized and the relevant detail extracted. The phase contrast halo which surrounds potentially mitotic cells is recognized using a series of transformations of the digitized image. The temporal and spatial relationships of the cell groupings from successive images are then analyzed to determine if mitosis has in fact occurred, and if so this fact is recorded.

By utilizing a series of transformations to identify the halo surrounding the mitotic cells, only the digital image information in the region immediately surrounding the specific cells is analyzed. In addition, the coordinates of each ringed (mitotic) cell identified, along with other relevant information, is recorded in a list in memory as each image frame is processed. In this way, detection of mitosis between pairs of cells which appear at different times is facilitated because the entire digital image from each frame does not need to be compared with all the others at the end of the observation period. All that is required is cross-comparison of the information stored in the list in memory to identify mitotic cell pairs.

The preferred embodiment of the present invention provides a means for electronically viewing an image, most advantageously a microscope fitted with a standard newvicon video camera. Also provided is a digital image processor for conversion of the video signal to digital information. This processor is most advantageously coupled to a general purpose digital computer. The digital computer performs the analysis of the digital image produced by the image processor. Algorithm means are provided both to transform the digital image (or portions thereof) within the processor memory and to detect an actual event of mitosis. Detected incidences of mitosis are recorded in computer mass storage memory for later display on a standard device, such as a cathode ray tube (CRT) or printer.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of the present invention.

FIG. 2 is a block diagram of a digital image processor used in the present invention.

FIG. 3a illustrates a typical arrangement of the memory in the internal RAM of the host computer in the present invention.

FIG. 3b illustrates a typical arrangement of a segment of the memory in the digital image processor of the present invention.

FIG. 4 illustrates the four structuring elements used to make a grey image ridge extraction as contemplated for the present invention.

FIG. 5 illustrates a two-dimensional thin-ring-shaped structuring element used in the RT-transformation.

FIG. 6 illustrates the data structure for application of decision rules for mitotic events.

FIG. 7 illustrates the dilation of an image by a structuring element.

DETAILED DESCRIPTION

Notation and Nomenclature

The detailed descriptions which follow are presented largely in terms of algorithms and symbolic representations of operations on data bits within a computer memory. An algorithm is here, and generally, conceived to be a self-consistent sequence of steps leading to a desired result. These steps are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It proves convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like. It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities.

Further, the manipulations performed are often referred to in terms, such as adding or comparing, which are commonly associated with mental operations performed by a human operator. No such capability of a human operator is necessary, or desirable in most cases, in any of the operations described herein which form part of the present invention; the operations are machine operations. Useful machines for performing the operations of the present invention include general purpose digital computers or other similar devices. In all cases there should be borne in mind the distinction between the method operations in operating a computer and the method of computation itself. The present invention relates to method steps for operating a computer in processing electrical or other (e.g., mechanical, chemical) physical signals to generate other desired physical signals.

The present invention also relates to apparatus for performing these operations. This apparatus may be specially constructed for the required purposes or it may comprise a general purpose computer as selectively activated or reconfigured by a computer program stored in the computer. The algorithms presented herein are not inherently related to any particular computer or other apparatus. In particular, various general purpose machines may be used with programs written in accordance with the teachings herein, or it may prove more convenient to construct more specialized apparatus to perform the required method steps. The required structure for a variety of these machines will appear from the description given below.

The specific algorithms described below are most conveniently expressed using the formal notation of morphology, a mathematical analysis which embodies sets in digital space. This formalism provides a concise mode of presentation by repeated reference to a physical model of a digital image composed of stacks of boxels (cubes). A two dimensional picture represented by a rectangular array of pixels, each with a distinct gray value, would thus be modelled by a three dimensional solid where, at each Cartesian x,y-coordinate there is a stack of boxels (stacked in the Cartesian z-direction), the height of which corresponds to the gray value of the picture (0-255) at that coordinate. In the following dictionary of terms, X and Y refer to sets and x and y to boxels. When Cartesian directions are meant, they will be explicitly so labelled. Image analysis amounts to set transformations, where the elements of the sets are the discrete image boxels. A more detailed description of the notation of morphology can be found in the comprehensive text by J. Serra, Image Analysis and Mathematical Morphology, London, Academic Press, Inc., 1982.

For the purpose of understanding the present invention, the following notations will be used, which notations have the respective meanings set forth below.

x a boxel

X a set of boxels

x X set membership; boxel x belongs to set X

X Y set X is included in set Y

X.andgate.Y set intersection; set of boxels belonging to both set X and set Y

f numerical function defining the gray value at each Cartesian x,y coordinate of the picture; they may be thought of as a three dimensional surface defining the upper contours of the three dimensional boxel model.

X.sub.t (f)the family of two dimensional sets, called sections, composed of the boxels present at height t, for 0.ltoreq.t.ltoreq.255. The family of such sets taken as a whole comprises the umbra of the function and generates the function.

X.sym.B dilation of set X by structuring element B (also a set). For a two dimensional set, for example: translate B to every image point in turn. If any element of B hits (touches, overlays) any element of X then mark the hit at the image point corresponding to the current position of the origin of the structuring element. The set of such marks represents the dilated set. For example, ##EQU1## For a three dimensional set such as the umbra of a function f, dilation by a two dimensional structuring element, in principle, is accomplished by applying the two dimensional dilation to each X.sub.t in the family of X.sub.t 's, 0.ltoreq.t.ltoreq.255 for the function f. The result is a new family of sets, X.sub.t (f.sym.B), which are the sections of the function (the picture) dilated by the two-dimensional structuring element B. In practice, the dilation for a picture is accomplished by first considering the surface defined by the topmost boxels (the function f) to be an impenetrable barrier. The structuring element then roams about above the surface: at each image Cartesian x,y coordinate the structuring element is lowered until it first hits the surface at any boxel; the height of the origin of the structuring element is noted at that particular Cartesian x,y coordinate; the picture thus calculated is the dilated picture. The manner of computing this is straightforward. Each pixel in a picture is replaced by the maximum gray value found at relative positions in its immediate neighborhood which correspond to similar relative positions of the structuring element.

f-g difference to two functions; the resulting function is a picture (no negative gray values) if and only if X.sub.t (g) X.sub.t (f), where 0.ltoreq.t.ltoreq.255 (i.e., g.ltoreq.f at every Cartesian x,y coordinate).

X.sub.t (f).andgate.X.sub.t (g) intersection of the sections of two pictures; this is computed by taking the minimum value, pixel-by-pixel in comparing pictures f and t. The sections of the resulting picture will be the desired set intersection.

The following description is divided into several sections. The first of these will discuss the general configuration of a system for processing digital images. Later sections will address specific aspects of the present invention, such as extraction of the relevant detail, identification of the phase contrast halo by image transformations, and analysis of the relationships between identified cells to detect mitosis.

GENERAL SYSTEM CONFIGURATION

FIG. 1 shows a typical computer-based system for image processing according to the present invention. Conversion of optical images to electronic signals is accomplished by the combination of a standard newvicon video camera 23 and a microscope 20 utilizing low intensity phase contrast illumination. This video camera and microscope are representative of generally available video and optical equipment for scientific use. In the system utilized by the inventors, the camera and microscope optics together result in a 600.times. magnification. Of course, it will be generally recognized that the magnification is a function of the type, and particularly the size of the cells used. In the present embodiment, the microscope and camera combination are used to observe mitosis in cell culture 24.

The video signal generated by camera 23 is coupled to digital image processor 27, shown in detail in FIG. 2. This image processor contains several internal elements for processing the incoming video signal and converting said signal to digital form. These elements include an internal processor (CPU) 40, capable of controlling all the internal components of the image processor, accepting commands from an external host computer, and transmitting a video signal, in digital form, to such host computer. An internal program memory 42 is included for storage of instructions and routines received from the host computer. The image processor also contains two look-up tables 45, two digital-to-analog converters 46, one 8 bit (256 possible gray levels) analog-to-digital converter 47, a binary shifter 49 and an image statistics unit 44.

Also shown are two 8-bit random access memories (RAMs) 41, and a 16-bit RAM 43. These RAM memories are used to store both raw (unprocessed) and processed digital image data, as well as other data derived during the analysis of the images. FIG. 3b shows a typical arrangement of 8-bit RAMs 41, including an Image Pixel Array 52 and a Degree of Closure Array 45, both used in processing the image as described in more detail below, space for the raw image data 57 and space for other data and spare memory 59.

The image processor shown is intended to be representative of the general class of devices for processing such signals under host computer control. A particular example of a suitable image processor is the EyeCom III model image processor manufactured by DBA (from Florida). Other image processors having similar capabilities are easily adapted for use in the present invention.

Digital information representing the processed video signal is transferred from the image processor to host computer 39 via direct memory access channel 30 (the "DMA"). These two elements are typically found in most general purpose computer systems and in many specialized computer systems. Any of numerous, easily available, data processors which provide for DMA are readily adaptable to use as shown.

Also shown in FIG. 1 is a mass storage device 34 connected to host computer 39. This mass storage device may be any of a number of available devices, including magnetic tape, magnetic disk, paper tape or cards. The data retained in mass storage 34 may be transferre