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.

Description

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.[0002] The United States Government has rights in this invention pursuant to Contract No. W-7405-ENG-48 between the United States Department of Energy and the University of California for the operation of Lawrence Livermore National Laboratory.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 pr...

AI Technical Summary

Problems solved by technology

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.

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.

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.

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