2. Methods

A general outline of the steps required for the acquisition of high magnification images is as follows:

Step 1.) Acquire low magnification (660x) image.

Step 2.) Identify potential features of interest.

Step 3.) Locate feature in center of field of view.

Step 4.) Autofocus at 38,000x.

Step 5.) Acquire high magnification (38,000x) image.

Step 6.) Assess quality of high magnification image.

High magnification images are acquired for every feature of interest identified in the low magnification image and the entire procedure is repeated for every square on the grid. Each of these steps will be described in more detail below.

2.1 Specimen Preparation

Catalase crystals were prepared based on the method of Sumners and Dounce [6]. A low magnification image [660x] of the catalase preparation is shown as fig. 1a. The preparation results in large rectangular areas of crystalline protein. However, the number of layers of protein is quite variable and the individual crystals often overlap. Various stain and contamination artifacts are also common.

2.2 Microscopy and Image Acquisition

Images were recorded using a Philips CM200 equipped with a Gatan MSC CCD. The cold trap liquid nitrogen dewar was modified to increase the cryogen capacity to enable the system to run overnight without refilling.

2.3 Low Magnification Image Acquisition

A low magnification image of each grid square is automatically acquired by systematically moving the goniometer to the center of each grid square in a spiral sequence. The size and orientation of the grid squares is determined from an initial calibration which takes only a few minutes to complete. This procedure can also be used as a stand alone application to automatically acquire low magnification preview images of an entire grid [7].

2.4 Low Magnification Feature Identification

Each low magnification image is processed to identify large contiguous areas of density by a method which uses cross correlation of the image with a template (fig 1b). Image feature metrics (size, mean, variance, centroid) are calculated and stored for each of the identified contiguous regions. These image features are later used in deciding whether a high magnification image of the region will be acquired; for example, regions that are too small are rejected. Within each acceptable contiguous region, a variety of methods can be used to select a target at which to acquire a high magnification image. The simplest method is to use the center of the region as a targeting selection and this works well for non-overlapping crystals.

Quite frequently, however, several crystals overlap and an additional analysis is used to model these clusters in order to identify the individual crystals. A mixture of orientation-adaptive, scale-adaptive masks are fit to the subimage using the expectation-maximization algorithm [8]. After testing out a number of masks, the algorithm selects the one that gives the best fit. Each mask identifies a specimen or a significant portion of a specimen and the centroid of each mask is taken as a possible target location for obtaining a high-magnification image. Fig. 2 illustrates how this pattern recognition algorithm can identify two catalase crystals, despite the fact that they are overlapping. The orientation and size of each mask is then used to extract a normalized rotation-invariant, scale-invariant image of the corresponding specimen. These image features are used as a targeting selection criteria for subsequent high magnification image acquisition. For example, in fig. 2 a high magnification image is acquired for each of the two target points indicated.

One final method used for target selection was a computer assisted interface where the operator manually selected targets from the low magnification image. This was developed to compare the performance of the automated system with that of a human operator. These results are described below.

2.5 High Magnification Image Acquisition

A high magnification image is acquired at each of the targeting locations identified in the low magnification analysis. The first step is to center the selected feature within the field of view and this is achieved by repositioning of the goniometer. The goniometer has been calibrated and programmed to correct for backlash errors but its precision is still such that feature location can be inaccurate by up to 500nm (approximately within the specifications of the microscope). To compensate for these inaccuracies we have implemented a second refinement step in which correct positioning is verified by cross correlation with the targeted sub-region selected from the low magnification image. The magnification is then increased to 38,000x, the image is focused automatically using a beam tilt algorithm [9], and a high magnification image is acquired (fig. 3a).

2.6 High Magnification Image Quality Assessment

The image quality of each high magnification image is automatically assessed by calculating the power spectrum (fig. 3b), identifying diffraction spots (fig. 3c), and measuring the signal to noise ratio of each diffraction spot [10]. The machine quality rating for the image is equal to the number of diffraction spots with a signal to noise greater than 3.5 (grade 1 and 2 spots).

2.7 Leginon System Architecture

The Leginon system uses a real-time distributed computing architecture as shown in fig. 4 and it consists of the following layers:

EmScope Control Server: A multi-layered software library for portable and extensible control and acquisition in a distributed software environment [7]. A server has been developed using this library that provides distributed control of all functions on the microscope and camera.

Application Layer: A client application controls the entire data acquisition and analysis process. The application layer has been developed using Tcl/Tk [11] and integrates readily available image processing applications [12-13] as well as custom developed software. It integrates microscope and camera control, image analysis and processing, data management and the application logic. The application can be readily modified at the tcl scripting level.

User Interface: The application is monitored using a standard web browser that can be accessed by multiple users. The interface can display any of the steps in the acquisition process and is useful for assessing the progress of the experiment. Since data acquisition can run continuously for several days a web browser interface allows a means of assessing and monitoring the experiment from any convenient location.

Quality of Service Guarantees: A real-time distributed computing environment has been developed to (i) provide processor quality of service (QoS) guarantees and (ii) allow this application to share the computational resources with other non-real-time applications. The shared resource, CPU, is controlled by an adaptive real-time server (ART Server) [14]. The application accesses the ART server through the Tcl/Tk application layer as described above. We have implemented the ART server on an SGI O200 system that has two CPU processors running IRIX v 6.4.

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