IMAGE PATTERN EXTRACTION

Abstract: This invention describes methods and apparatus for the radial extraction and pattern recognition of image information based on ''back scatter'' produced by focused beam of energy such as those used in radar, sonar, and ultrasound. This invention includes methods for line and geometric pattern extraction and recognition from an image based on a pixel array. In the present invention, the information obtained from ''back scatter'' energy extracted and encoded by the radial extraction algorithms becomes determinants to be used either individually or in concert with each other depending upon the application and desired function. The line and geometric pattern algorithms can be used to process any type of image. These algorithms which extract and encode the patterns contained in the ''back scatter'' and geometric shapes contained in images are unique and provide an evaluation of various multidimensional aspects of groups of pixels in the resultant image. In addition, these algorithms are not based upon strict mathematical concepts but are rather translated to the computer in terms of bit strings which require very little processing time. The disclosed methods are designed for efficient image processing in real time on PC-type computer equipment.

Description: 1 IMAGE PATTERN EXTRACTION 2 BACKGROUND OF THE INVENTION 3 1. Field of the Invention , 4 This invention relates generally to a method and 5 apparatus for extracting information from the gray scale image , 6 containing "back scatter" energy generated by a focused beam of 7 energy (ultrasound, radar, sonar) striking an echoic surface 8 target. The ultrasound imaging of such as human tissue to recognize 9 and identify the resultant echoic characteristics of the target. 10 Specifically the invention relates to a method and apparatus for 11 inexpensively extracting significant image information based on 12 edges, lines, contours and geometrical shapes from an image 13 containing back scatter image patterns in real time. 14 15 2. Description of the Prior Art 16 The use of ultrasonic energy producing echoic information 17 concerning the health and well being of body organs and tissues is 18 well known. Gray scale eσhography which provides characteristics of 19 tissue texture in a visual image display representative of tissue 20 and cell differences due to disease or lesions is also known. A 21 beam of focused ultrasonic energy on a tissue target produces 22 parameters with respect to the tissue including attenuation and 23 back scattering. A complete discussion of texture and tissue 24 echograms can be found in the Journal of Ultrasound Medicine Volume 25 9, pages 215-229, 1990. 26 The present invention deals with echoic back-scatter 27 information as well as geometric line and shape information which 28 are contained in a processed image in a visual x and y coordinate 29 system on a cathode ray tube as a function of visually reproduced 30 pixels. In particular, computerized image processing and visual 31 image processing including pattern extraction and recognition is ,32 known. U.S. Patent 4,769,850 issued to Itoh et al September 6, 1988 33 shows an apparatus for extracting circular and linear components in 34 a complex computerized image. Likewise U.S. Patent 4,866,783 issued 35 to Ohyama September 12, 1989 shows a system for detecting edges of an image which includes a scanning circuit and other components for evaluating the image. U.S. Patent 4,817,171 issued to Stentiford March 28, 1989 shows a pattern recognition system for syntactic decision processing. U.S. Patent 4,908,872 issued to Toriu et al March 13, 1990 shows a method and apparatus for extracting contour lines from a pattern obtained in image processing using vector gradients for comparing gray level gradients among adjacent pixels. To date, the primary problem with the prior art systems are the mathematical complexity and the time and computing power required for extracting relevant image information cost effectively in real time. Using current image processing methods, geometric pattern extraction and recognition is extremely complex and a multi-step mathematical process which requires the power of a super computer to process images in real time. With respect to the ultrasonic imaging of target tissue, the interaction between the "back scatter" and the primary returning energy causes variations in the intensity of the primary returning energy which shows up in the resultant image as a textured surface with gray scale brightness differences represented by the pixel array relating to the relative intensity of the returning energy from the target at specific x and y positions. The present invention, for example, can be used for example to analyze an image produced by the ultrasonic scanning of a thin, planar area of tissue located within the focal length of the ultrasonic emitter. The invention analyzes the multidimensional patterns contained in the back scatter and the geometrical lines and shapes, i.e. geometrical patterns; all of which are contained in the image produced by the scanning energy beam. Topographical variations appear as the gray scale pixel luminence intensity variations distributed across the image area presented. The present invention discloses a method and apparatus using personal sized computers such as an IBM PC, to provide real time image analysis and evaluation to discern relevant information patterns in the back scatter component of the image for SUBSH T 1 differentiating cells and tissues which heretofore were not 2 discernible without a super computer or invasive surgery 3 accomplished at a resolution level to ensure the reliability of the , 4 procedure. 5 The present invention provides a less complex technique 6 for complex image processing by a novel examination of 7 predetermined target pixel radials extending from the target pixel 8 for analysis of surrounding pixel gray scale value variations 9 between the target pixel and neighboring near and far pixels 10 surrounding the target pixel. 11 BRIEF SUMMARY OF THE INVENTION 12 The invention described herein is a method and apparatus 13 for evaluating back scatter energy components contained in images 14 which form complex image patterns by examining near and far pixels 15 surrounding a target pixel radially as a function of edges, lines 16 and contours relative to radial pixels and their gray scale values. 17 Use of this information is then employed for line extraction, shape 18 extraction and shape recognition through the use of resultant 19 accumulated bit strings relating to the pixel edges, lines and 20 contours around the target pixel as a function of various 21 derivative bit string information. The resultant bit string 22 information can then be compared with an accumulated data base of 23 back scatter patterns to produce useful distinctions between 24 critical areas of cells and tissue differences within a target 25 group. 26 It is an object of this invention to provide an image 27 processing method for edge, line and contour pattern extraction 28 from a complex image containing back scatter patterns in real time 29 using a "small" personal computer. 30 It is another object of this invention to provide image 31 and pattern recognition in a complex image in a cost effective .32 apparatus and method. 33 Yet another object of this invention is to provide for ,34 extracting of information from back scatter energy intensity 35 variations produced by a beam of ultrasonic energy scanning tissue cells for tissue cell evaluation in a non-complex, cost-effective, real time process and apparatus. In accordance with these and other objects which will be apparent hereinafter, the instant invention Will now be described with particular reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 shows a schematic diagram of back scatter energy from a focused beam of energy striking an irregular echoic surface. Figure 2 shows a diagram of near and far neighboring pixels surrounding a target pixel from an image for radial examination of particular near and far neighboring pixels. Figure 3 shows a diagram of the gray scale value for the target pixel for the radials defined in Figure 2 showing edges found along each radial. Figure 4 shows a flow diagram for radial shape extraction used in the present invention. Figure 5 shows a diagram of the resultant accumulated bit string utilizing the radial shape process for the target and neighboring pixel patterns shown in Figures 3 and 4. Figure 6 shows a flow diagram for radial contour extraction utilized in the present invention. Figure 7 shows radial contour calculation diagram relating to the flow diagram of Figure 6. Figure 8 shows flow diagram for radial asymmetry extraction utilized in the present invention. Figure 9 shows radial asymmetry calculation diagram for the flow diagram of Figure 8 including the resultant accumulated differences. Figure 10 shows the flow diagram for the radial gradient extraction used in the present invention. Figure 11 shows a radial gradient calculation diagram related to the flow diagram of Figure 10. Figure 12 shows the flow diagram for line acquisition to locate the beginning of a line in an image. 1 Figure 13 shows a line following flow diagram to follow 2 a line in an image and measure the line length and number of 3 changes in direction contained in the line wherein each change in 4 direction must be less than ninety degrees. 5 Figure 14 shows the second part of the flow diagram 6 contained in Figure 13 related to line extraction. 7 Figure 15 shows two contiguous lines in an image for line 8 extraction pixel evaluation for line extraction. 9 Figure 16 shows line extraction calculations related to 0 Figures 12, 13, 14 and 15. 1 Figure 17 shows a flow diagram for geometric shape extraction utilized in the present invention to encode the geometric shape into a set of bit strings describing the number of direction changes for each line and the length of each line contained in the geometric shape. Figure 18 shows a pixel diagram of a circle. Figure 19 shows a circle shape calculation diagram for processing each pixel contained in the perimeter of a circle shown in Figure 18 by geometric shape extraction and the resultant accumulated bit string. Figure 20 shows a square shape pixel diagram. Figure 21 shows a square shape calculation diagram based on the diagram shown in Figure 20 for a geometric shape extraction and resultant accumulated bit strings. Figure 22 shows a triangular shape pixel diagram. Figure 23 shows the pixel processing triangular shape calculation diagram for pixel processing of the diagram shown in Figure 22 and the resultant accumulated bit strings. Figure 24 shows a schematic diagram of a geometric shape recognition knowledge tree utilizing circular, triangular and square pixel images for storage in a knowledge base. Figure 25 is a schematic diagram of a system using applicant's invention. DESCRIPTION OF THE PREFERRED EMBODIMENT OF THE INVENTION Referring now to the drawings, Figure 1 shows a schematic diagram which has an ultrasonic emitting device probe 5 which bombards the irregular surface 3 (which could be tissue) to obtain a return echo of intensity 4 based on the direct beam of energy 2 striking the target causing an interaction of the reflected energy l with the returning beam 2. Although the returning energy based on the interaction between the back scatter (produced by the irregular surface) and the primary returning energy is not predictable, it is repeatable when compared so that processing the image based on the intensity levels related by gray scale pixel values produce useful information regarding the target surface. The purpose of the invention is to process the returning energy to provide useful information relating to the characteristics of the target tissue. Referring now to Figure 2 the center target pixel surrounded by a first layer of pixels N1-N8 representing near neighbor pixels and two additional outer groupings of pixels surrounding in a second and third tier level from the target pixel represented by F1-F43. In utilizing the present invention a plurality of imaginary radials 1-8 and 10-17 are selected which will be used in evaluation of the surrounding near and far neighboring pixels. These are the groups of pixels to be processed in accordance with the present invention. Figure 3 shows an example of a plurality of pixels along the radial selected, each having numerical values relating to their gray scale value. The target pixel has an arbitrarily selected numerical gray scale value of 9. The purpose of this diagram is for explanation purposes only; it does not represent any true or absolute values to be found in analyzing back scatter. An edge is defined as a difference in gray scale value between the target pixel and the near or far neighboring pixels along the radial selected. "RADIAL SHAPE EXTRACTION" Figure 3 shows a group of pixels from an image generated by one of the aforementioned imaging systems. The "Radial Shape Extraction" algorithm (Figure 4) evaluates the NEAR and FAR neighboring pixels along the imaginary radials extending from the SUBSTITUTESHEET 1 target pixel. The NEAR neighbors are the pixels directly adjacent 2 to the target pixel. The FAR neighbors are the second and third 3 ring of pixels surrounding the target (see Figure 2) . The algorithm 4 shown in Figure 4 searches each radial for an edge; i.e. a 5 difference in gray scale value between the target pixel and the 6 near and/or far neighbors along the radial selected (see Figure 4 7 #30-37) . Each edge position relative to the target is noted as a 8 bit string which is appended to an accumulated string of bits (see 9 Figure 4 #38) . Processing each of the eight radials (see Figure 4 10 #32) as shown in all of Figure 4 creates an accumulated string of 11 bits analogous to the "RADIAL SHAPE" of the edges. These edges in 12 all the radials could be considered as dots in a "connect the dots 13 to form a shape" child's puzzle. Sequential processing by the 14 algorithm shown in Figure 4, of each radial shown in Figure 3, 15 results in the bit string sequence shown in Figure 5. 16 "RADIAL SPATIAL DISTRIBUTION EXTRACTION" 17 The "Radial Spatial Distribution Extraction" algorithm 18 measures the general distribution of the edges found in the near 19 and far pixels surrounding and in reference to the target pixel. 20 This is accomplished by simply counting the number of "0's" in the 21 bit string generated by the "Radial Shape Extraction" algorithm. 22 These zeroes are analogous to the distance of the edges from the 23 target pixel. A number from 0 to 24 is calculated as an indication 24 of the distribution and complexity of the pattern of the edges 25 produced by the "Back Scatter". The larger the number calculated, 26 the more widely distributed the edges; ergo, a smaller number 27 represents a smaller "Spatial Distribution" of edges. The "Spatial 28 Distribution" of the edge pattern produced by the "Back Scatter" is 29 directly related to the echoic surface topography of the target 30 being imaged. 31 The bit string generated by the "Radial Shape Extraction" • 32 algorithm in Figure 5 contains eight "1" bits and two "0" bits; 33 therefore, said bit string has a "Radial Spatial Distribution" of . 34 two. 35 "RADIAL CONTOUR EXTRACTION" SUBSTITUTESHEET The "Radial Contour Extraction" algorithm measures the delta (change) in the edges identified by the "Radial Shape Extraction" algorithm. The edge delta is related to the echoic homogeneity of the target being imaged. This is accomplished by calculating the accumulated variations in edge pixel values shown in Figure 3 using the algorithm shown in Figure 6 #50-58. This produces a number from 0 to 248 in an image based on 32 gray scales (i.e. 0 = black, 31 = white). The lower the number, the MORE homogeneous the target is; ergo, the larger the number, the LESS homogeneous the target is. Figure 7 shows the "Radial Contour Extraction" calculation diagram for the pixels identified in Figure 3 by the "Radial Shape Extrication" algorithm. As shown in the calculation diagram in Figure 7, the accumulated difference is 16, a number indicating echoic homogeneity. "RADIAL ASYMMETRY EXTRACTION" The "Radial Asymmetry Extraction" algorithm shown in Figure 8 #70-77 measures the asymmetry between opposing imaginary radial pairs extending from the target pixel. The algorithm assumes eight imaginary radials of pixels extending from the target pixel much like spokes in a wheel (see Figure 2 #10-17) . Each radial is three pixels in length (see Figure 3 #20-27) . The gray scale value of the pixels along the radials is compared with that of their counterparts and the differences are accumulated for all four radial pairs as illustrated in the "Radial Asymmetry Calculation Diagram" shown in Figure 9. This process produces a number analogous to the overall asymmetry of the pixels surrounding the target pixel and is an abstract measure of the echoic asymmetry of the target being imaged. "RADIAL GRADIENT EXTRACTION" The "Radial Gradient Extraction" algorithm measures the delta (change) in gray scale from radial to radial. This algorithm assumes eight imaginary radials of pixels extending from the target pixel much like spokes in a wheel (see Figure 2 #10-17) . Each radial is three pixels in length (see Figure 3 #20-27) . The purpose WBsmiTurc SHEET of this algorithm is to measure gradual changes in echogenicity as the target being imaged is scanned by the energy beam without the need to take into Consideration the scanning axis. The steps by which to measure these changes are shown in the flow diagram for the "Radial Gradient Extraction" algorithm in Figure 10 #90-99. Figure 11 is an example of a gradient value obtained from the gray scale values of pixels along the eight imaginary radials given in Figure 3. "LINE EXTRACTION" The "Line Extraction" algorithm evaluates edges extending from the target pixel in any direction which form a progressive set of contiguous edges. The end of the line is the point at which the contiguous edges change direction 90 degrees or more. This algorithm has been included in 3 Figures 12-14 to facilitate the steps necessary to ascertain the beginning of a line (Figure 14 #110-123), and, if a line is found (Figure 13 #130-145), to follow the line to determine if any changes in direction greater than 89 degrees have occurred; thus indicating the end of the line (Figure 13 #150-157) . An example of "Line Extraction" is shown in the "Line Extraction Pixel Diagram" in Figure 15 #160-163 wherein the beginning and ending of the line #160 is shown to be pixel x-17, y-3 (the target pixel) and pixel x-8, y-12, the pixel at which a change of direction greater than 89 degree takes place. A new line begins at #162 and continues on as line #161 in the direction of the arrow to end at pixel x-14, y-12. Using the "Line Extraction" algorithm, a square would then have 4 lines resulting from 4 changes in direction of 90 degrees or greater while a circle would be comprised of only one line based upon changes in direction of less than 90 degrees. Specifically, the algorithm creates a string of bits, where each bit represents each pixel contained in the line. As shown in the actual "Line Extraction Calculation Diagram" in Figure 16, the string of bits contains "zeros" where no change in direction from pixel to pixel has occurred and "ones" where a change in direction has occurred. Two numbers result from these calculations: the first is the length of the line, i.e. the number SUBSTITUTE SHEET of pixels contained in the line which is designated as "String Length" in Figure 16; and the second, the number of changes in direction in the line, i.e. the number of "ones" in the bit string (see Figure 16, column 6) . When these numbers and bit strings are used to form a "Geometric Knowledge Base", the numbers facilitate fast searches of this Knowledge Base for possible pattern match candidates. "GEOMETRIC SHAPE EXTRACTION" The "Geometric Shape Extraction" algorithm uses the "Line Extraction" algorithm to create a series of bit strings, one for each line contained in the shape, to define the shape being extracted. This is shown in the "Shape Extraction Flow Diagram" in Figure 17 #180-192. A shape is a group of contiguous lines that meet at the point of origin. For example. Figure 18 shows the pixel of origin (colored black) to be also the last pixel in a line which forms a circle. Figure 19, which is the "Circle Shape Calculation Diagram", shows that there is a line count of only one for a circle and also gives the line length and direction changes expected for a circle, which are summed up as the accumulated bit pattern shown in column 5. In like manner, the algorithm can be used to determine other shapes such as a square (see Figure 20) , which can be observed to have 4 lines as shown in the "Square Shape Calculation Diagram" in Figure 21; and a triangle (Figure 22) , which can be calculated by this algorithm to have 3 lines. This information is shown in the "Triangle Shape Calculation Diagram" in Figure 23. The numbers and bit strings extracted by the "Line Extraction" algorithm describe, in an abstract sense, the characteristics of each line contained in the shape. These numbers and bit strings are appended together to define a shape. For the purpose of high speed comparison candidate searches in a "Knowledge base" using this type of data, a set of composite numbers is calculated and appended to the overall group of numbers and bit strings which define the shape. The composite numbers calculated indicate the number of lines contained in the shape, the size of the shape expressed in pixels, and the total number of changes in directions contained in SUBSTITUTESHEET the shape. "GEOMETRIC SHAPE AND PATTERN RECOGNITION" Using the "Line Extraction" (Figures 12-14) and ' "Geometric Shape Extraction" (Figure 17) algorithms, a root knowledge base can be generated. This stored knowledge base describes in an abstract sense various geometric shapes and patterns. The knowledge base data is stored in logic tree format based on the line count and the number of direction changes in that order. Figure 24 shows such a knowledge base containing a circle, a rectangle, and a triangle. Each major category of geometric shapes becomes a main branch of the logic tree (Figure 24 #260) . The major categories are based on the line count contained in the shape. Minor categories then become minor branches of the major branches based on the number of changes in direction contained in the shape. Once a geometric shape has been extracted from a target image using the "Geometric Shape Extraction" algorithm, the resultant line count and changes in direction can then be used to search the knowledge tree to select bit pattern match candidates. The size of each candidate or the target, depending on which bit pattern is larger, is adjusted to match the size of the other by removing every N-th bit from the larger of the two. The bit patterns contained in these candidates are then compared on a bit by bit basis with the target image shape extracted. A percentage score is then calculated for each candidate based on the number of bits in the respective bit pattern tables that match. The candidate with the highest score is selected as the best match from the knowledge base and is assumed to be the closest geometric approximation to the target shape. The instant invention has been shown and described herein in what it is considered to be the most practical and preferred embodiment. It is recognized, however, that departures may be made therefrom within the scope of the invention and that obvious modifications will occur to a person skilled in the art. SUBSTITUTESHEET

Claims: I claim: 1. An image processing method for rapidly selecting pattern information from a pixel generated image comprising the steps of: (a) selecting a target pixel relating to target image to be evaluated; (b) selecting a first group of pixels directly surrounding said target pixel; (c) selecting a second group of pixels surrounding contiguously said first group of pixels; (d) assigning one or more radials from said target pixel through at least one pixel of said first group and continuously through at least one pixel of said second group; (e) determining the pixel gray scale values along each radial in group one and group two to determine the number of edges along each radial; (f) providing a bit string volume based on the number of edges in each radial; and (g) accumulating all of the radial bit string values into an accumulated string of bits relating to the radial shape of the edges. 2. A method for extracting pattern information from edge radial spatial distribution relating to an image of pixels in x and y coordinates as in claim 1 comprising the additional steps of: (h) counting the number of "0" bits in each accumulated bit string; (i) assigning a numerical value to the resultant "0" bit count relating to the spatial distribution of edges. 3. A method as in claim 1 for radial contour extraction of the pixel target image comprising the additional steps of: (j) identifying the gray scale pixel value differences on each radial between the target pixels and the edge pixels; (k) summing the total absolute gray scale pixel value differences for all radials relating to the target pixel, whereby SUBSTITUTE SHEET the resultant total pixel gray scale differential value indicates image homogeneity around said target pixel. 4. A method for extracting pattern recognition information from a pixel image formed by an x and y coordinate pixel array comprising the steps of: (a) selecting the target pixel in said pixel array; (b) delineating matched pairs of colinear radials originating from said target pixel, each said radial being at least three pixels in length; (c) determining the gray scale pixel differences between each pair of colineal radials as an absolute numerical value; (d) accumulating the total gray scale differences for all radial pairs to ascertain the total gray scale pixel asymmetry surrounding said target pixel relating to the echoic asymmetry of an image pattern around the target pixel. 5. A method of image processing for pattern recognition from a pixel image array in x and y coordinates for radial gradient extraction as in claim 1 in order to measure gradual changes in echogenicity comprising the additional steps of: (h) adding the absolute value of gray scale values of at least three pixels along each radial; (i) determining the difference in total gray scale values between adjacent radials; (j) summing the total of the absolute differences in adjacent radial gray scale values to obtain a final accumulated gradient value. 6. A method for extracting lines from an image formed by a plurality of pixels in an x and y coordinate image pixel array comprising the steps of: (a) selecting a target pixel; (b) selecting a plurality of target radials emanating from said target pixels; (c) determining a radial with an average pixel gray scale value within a predetermined value relative to said target pixel gray scale value along said radial; (d) assigning a bit value to each pixel located in said line as determined along said radial; (e) locating a change in direction of said line above 89°; (f) accumulating a bit string for said line evaluation. 7. A method for pattern recognition in image processing for use with an image formed by pixel array in x and y coordinates for geometric shape extraction as in claim 5 including the steps of: (k) search the accumulated bit strings for a line identification at each pixel location; (1) examine each line to find out when the target pixel location equals the origin of the line examination; (m) continue the line examination for all pixels until the target initial pixel equals the original line pixel whereby a shape will be extracted. 8. An image processing method for pattern recognition from a pixel image array in x and y coordinates for geometric shape and pattern recognition as in claim 6: (g) generating a logic tree knowledge base on line count and number of direction changes for recognition of geometric shapes based on the number of changes in direction and shape; (h) searching the knowledge tree data using resultant line count and changes in direction to select bit pattern match candidates; (i) adjusting the size of each match candidate by removing every N-th bit from the larger of the two; (j) comparing bit patterns on a bit by bit basis with the target image shape extracted. 9. An apparatus for the extraction of pattern recognition information from a pixel image containing back scatter energy SUBSTITUTE SHEET produced by a focused beam of energy (radar, sonar, ultrasonic, laser) emitted against a target such as human tissue to extract characteristic information concerning tissue being targeted, the apparatus comprising: means for generating a beam of ultrasonic energy; means for evaluating back scatter of said energy from a target as a function of the pixel gray scale values in a visual pixel array; means for generating a pixel array pattern in x and y coordinates as a function of back scatter intensity energy received from a specific target illuminated with a focused beam of energy; means for selecting a target pixel in said image pattern; means for selecting predetermined radials relative to said target pixel; means for determining the pixel gray scale values along each radial relative to said target pixel and pixels surrounding said target pixels to determine the number of edges along each radial; and means for providing bit string volume based on the number of edges in each radial and accumulating all the radial bit string values into an accumulated string of bits relating to the radial shape of the edges.

Inventors: GOUGE JAMES O, GOUGE, JAMES, O.
Applicants: GOUGE JAMES O, GOUGE, JAMES, O.
Application year: 1991
Main classification: G06K9
IPC codes: G06T5