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  Automated macromolecular crystal detection system and methodUSPTO Application #: 20070122025Title: Automated macromolecular crystal detection system and method Abstract: An automated macromolecular method and system for detecting crystals in two-dimensional images, such as light microscopy images obtained from an array of crystallization screens. Edges are detected from the images by identifying local maxima of a phase congruency-based function associated with each image. The detected edges are segmented into discrete line segments, which are subsequently geometrically evaluated with respect to each other to identify any crystal-like qualities such as, for example, parallel lines, facing each other, similarity in length, and relative proximity. And from the evaluation a determination is made as to whether crystals are present in each image. (end of abstract)
Agent: James S. Tak Assistant Laboratory Counsel - Livermore, CA, US Inventors: Allen T. Christian, Brent Segelke, Bernard Rupp, Dominique Toppani USPTO Applicaton #: 20070122025 - Class: 382141000 (USPTO) Related Patent Categories: Image Analysis, Applications, Manufacturing Or Product Inspection The Patent Description & Claims data below is from USPTO Patent Application 20070122025. Brief Patent Description - Full Patent Description - Patent Application Claims
I. CLAIM OF PRIORITY IN PROVISIONAL APPLICATION [0001] This application claims priority in provisional application filed on May 30, 2002, entitled "Augmented Automated Macromolecular Crystal Detection from Light Microscopy Images" Ser. No. 60/385,210, by inventors Christian et al. II. FIELD OF THE INVENTION [0003] The present invention relates to automated image recognition and detection systems and methods, and more particularly to an automated system and method using a phase-based edge detection process to detect macromolecular crystals from two-dimensional images obtained from light microscopy of crystallization experiments. III. BACKGROUND OF THE INVENTION [0004] Proteomics is the field of bioscience involving the characterization of the proteins encoded by the human genome, and enabled by the gene sequence data produced by the human genome project. Since the structure of a protein is key to understanding its function, one field of proteomics in particular has rapidly emerged concerning high throughput structure determination or structural genomics. In determining protein structure, the proteins are first crystallized, and then an X-ray generator produces diffraction patterns from which a three-dimensional picture of the atomic arrangement in the crystal can be obtained. Advances in macromolecular crystallography techniques, computer speed, and the availability of high-energy synchrotron x-ray sources, make rapid structure determination possible given adequate quality protein crystals. [0005] Crystal growth, however, is difficult because proteins are large, irregularly shaped molecules that do not readily come together in a repeating pattern, and the complete set of crystallization conditions is too large and impractical to screen comprehensively. Thus, previously uncrystallized proteins must be screened on a trial and error basis against a large array of conditions that have the potential to induce crystal formation. Automated methods using, for example, robotic liquid handling devices, robotic CCD-based microscope cameras, or light microscopes equipped with robotic stages and CCD cameras, have been developed and are commercially available to speed up the process of setting up and recording the results (automated image capture) of a large number of crystallization trials. However, a practical problem remains in that each experiment must still be visually inspected to determine successful crystal formation. In fact, the high throughput enabled by the automation in setup and image-capture has increased the visual inspection bottleneck, which is typically performed manually by human intervention. [0006] One example of an automated crystal detection method developed to address the visual inspection bottleneck is disclosed in the article "Intelligent Decision Support for Protein Crystal Growth" (by Jurisica et al, IBM Systems Journal, Vol. 40, No. 2, 2001). In that article, and as shown in FIGS. 3-7 thereof, images of screening results are analyzed using a two-dimensional Fourier transform. In particular, FIG. 5C illustrates the Fourier frequency spectrum used in the analysis, and FIG. 5D illustrates an analysis of the spectrum derivatives and circular averages to provide features information of the image. From this feature extraction and analysis, the outcome of the experiment is classified as, clear drop, amorphous precipitate, phase separation, microcrystals, crystals, or unknown. [0007] Despite such efforts, difficulties in automating (i.e. without human intervention) crystal detection remain due to such factors as poor image quality due to noise and low contrast, differences in crystal shapes, poorly formed crystals, etc. With respect to poor image quality, crystals may have less contrast relative to the background than other objects or particles. For example, the difference between the crystal and the background based on 256 gray levels is often 15 levels, whereas the difference for dirt is usually above 40 levels. Additionally, many different crystal shapes exist due to, for example, the existence of several large classes of crystal shape, the picture is a 2-D projection of a 3-D object, crystal imperfections with faulty edges, and large variations in crystal size, e.g. ranging from about 10 .mu.m to nearly 300 .mu.m. There are also many things on the picture that are not real crystals, such as dirt, precipitation, quasi-crystals, small drop due to condensation, and unidentified effects. Additionally, an automated crystal detection process must also achieve a high threshold of accuracy by being able to identify virtually all crystals with a low false-positive rate. [0008] Thus in summary, there is a need for an automated crystal detection method and system for inspecting two-dimensional images and successfully detecting crystals therefrom. An automated solution for crystal detection, such as implemented by a software program, would be a great labor savor by possessing the capability of processing thousands of images a day and provide analysis substantially free from false positives. IV. SUMMARY OF THE INVENTION [0009] One aspect of the present invention includes a method of detecting macromolecular crystals in light microscopy images comprising: detecting edges in said images by identifying local maxima of a phase congruency-related function associated with each image; segmenting the detected edges into discrete line segments; evaluating the geometric relationships that the line segments have with each other to identify any crystal-like qualities; and determining the presence of crystals in each image based on said evaluation. [0010] Another aspect of the present invention includes a computerized system for detecting macromolecular crystals from light microscopy images comprising: a digital conversion component that converts said light microscopy images into corresponding phase-based digital image data using the Fourier transform; an edge detection component that detects edges from the image data by computing local maxima of a phase congruency-related function associated with each image; a segmentation component that divides the detected edges into discrete line segments; and a geometric analyzer component that evaluates the geometric relationships that the line segments have with each other to identify any crystal-like qualities, and determines whether crystals are present in each image based on said evaluation. [0011] Another aspect of the present invention includes a computerized system for detecting macromolecular crystals from light microscopy images comprising: means for digitally converting said light microscopy images into corresponding phase-based digital image data using the Fourier transform; means for detecting edges from the image data by computing local maxima of a phase congruency-related function associated with each image; means for dividing the detected edges into discrete line segments; means for evaluating the geometric relationships that the line segments have with each other to identify any crystal-like qualities; and means for determining the presence of crystals in an image from said evaluation. [0012] Another aspect of the present invention includes a computer program product comprising: a computer useable medium having a computer readable code embodied therein for causing the detection of macromolecular crystals in light microscopy images, said computer program product having: computer readable program code means for causing a computer to detect edges in said images by identifying local maxima of a phase congruency-related function associated with each image; computer readable program code means for causing said computer to segment the detected edges into discrete line segments; computer readable program code means for causing said computer to evaluate the geometric relationships that the line segments have with each other to identify any crystal-like qualities; and computer readable program code means for causing said computer to determine the presence of crystals in each image based on said evaluation. [0013] Another aspect of the present invention includes an article of manufacture comprising: a computer useable medium having a computer readable code means embodied therein for causing the detection of macromolecular crystals in light microscopy images, said computer readable code means in said article of manufacture comprising: computer readable program code means for causing a computer to detect edges in said images by identifying local maxima of a phase congruency-related function associated with each image; computer readable program code means for causing said computer to segment the detected edges into discrete line segments; computer readable program code means for causing said computer to evaluate the geometric relationships that the line segments have with each other to identify any crystal-like qualities; and computer readable program code means for causing said computer to determine the presence of crystals in each image based on said evaluation. [0014] And another aspect of the present invention includes a program storage device readable by a machine, tangibly embodying a program of instructions executable by the machine to perform method steps for detecting macromolecular crystals from light microscopy images, said method steps comprising: detecting edges in said images by identifying local maxima of a phase congruency-related function associated with each image; segmenting the detected edges into discrete line segments; evaluating the geometric relationships that the line segments have with each other to identify any crystal-like qualities; and determining the presence of crystals in each image based on said evaluation. V. BRIEF DESCRIPTION OF THE DRAWINGS [0015] The accompanying drawings, which are incorporated into and form a part of the disclosure, are as follows: [0016] FIG. 1 is an overview flow diagram of a general embodiment of the present invention. [0017] FIG. 2 is a flow diagram detailing an exemplary edge detection process using phase congruency, and including a noise reduction subprocess. [0018] FIG. 3 is a flow diagram detailing an exemplary geometric analysis/evaluation process which evaluates the geometric relationships that the line segments have with each other to identify any crystal-like qualities. [0019] FIG. 4 is a flow diagram of a second exemplary embodiment of the present invention. [0020] FIG. 5 is a graph of a 1-D step signal and its cosine decomposition via the Fourier transform. Continue reading... Full patent description for Automated macromolecular crystal detection system and method Brief Patent Description - Full Patent Description - Patent Application Claims Click on the above for other options relating to this Automated macromolecular crystal detection system and method patent application. ###  How KEYWORD MONITOR works... a FREE service from FreshPatents1. Sign up (takes 30 seconds). 2. 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