OPTIMAS 6.5 is 32-bit software for image analysis in Windows 95/98 and Windows NT 4.0. It offers powerful processing and measurement capabilities, output to files or Excel, scripting, programming, hardware compatibility, and monochrome or color images. It supports high bit depth, image sequences, complex regions of interest, advanced morphology, Visual Basic, networks and more.

What is OPTIMAS?

OPTIMAS 6.5 is software for image analysis and laboratory automation. OPTIMAS combines the power of a comprehensive image analysis toolset with the flexibility of a developer's package in one easy-to-use environment.

OPTIMAS offers hundreds of powerful image processing and measurement functions, automatic data output to ASCII files or Excel worksheets, a powerful programming environment with automatic scripting, and compatibility with industry standard capture hardware and cameras for monochrome and color images.

OPTIMAS also supports high bit depth images, image sequence analysis, advanced morphology, and Microsoft Visual Basic compatibility. Operating as a native 32-bit application, OPTIMAS 6.5 takes full advantage of the processing capabilities of 32-bit operating systems such as Windows 95 and Windows NT.

Applications

OPTIMAS is being used by customers worldwide for a wide variety of applications, including:

Why use OPTIMAS?

OPTIMAS lets you:

OPTIMAS is at the heart of an image analysis system

OPTIMAS provides you with an extensive set of tools - over 400 functions - for image processing, measuring, counting, and sorting and the ability to easily manage multiple images, views and measurements.

Custom user interface development tools are included; you can draw dialog boxes in any Windows resource tool and link them to any OPTIMAS function. Alternately, you may opt to control OPTIMAS with programs you develop in other development environments such as Visual Basic, Visual C++, or Delphi.

OPTIMAS' powerful macro scripting and editing capabilities make up only part of its development environment; OPTIMAS includes custom application development tools to create image processing and measurement routines, as well as commands for communication and device control. In addition, you can control other Windows applications and libraries via DDE and DLLs.

Solving Your Application with OPTIMAS

Solving your application with OPTIMAS consists of three basic steps:

1. CONFIGURATION
2. DATA COLLECTION
3. REPORTING AND MANAGING RESULTS

Step 1: CONFIGURATION

The first step in solving an application with OPTIMAS consists of configuring it for use with your hardware and for the particulars of your application.

Image Capture

OPTIMAS is often used in conjunction with a wide variety of technical imaging equipment. OPTIMAS is designed to make data acquisition simple; its seamless interface supports virtually any image acquisition source, including digital and analog frame grabbers, scanners, PhotoCD, VCRs, or any other video source. If you plan to acquire images from cameras or scanners, first ensure that the cameras, frame grabbers, light sources, and other hardware you're utilizing are working properly prior to commencing data collection.

Calibration

Next, you should check OPTIMAS' calibrations and, if necessary, re-calibrate it before collecting data. Spatial calibration is the process of supplying OPTIMAS with a scale for establishing a size relationship for objects in the image frame. You can also apply a multiple-point calibration to correct for stretched or warped surfaces. You can apply grayscale calibration for relating pixel intensity to physical units such as chemical absorption. In addition, you can create a list of custom calibrations for the different applications or levels of magnification you expect to encounter. The OPTIMAS interface lets you manage one or more images and easily associate the calibration you specify with the currently active image.

Configuration Files

Finally, you can utilize OPTIMAS' configuration files to ensure repeatability. By saving settings such as the image's calibration, luminance range, LUTs, input channel, scale, threshold, etc. into a configuration file, you can ensure complete consistency in your image analysis applications.

Step 2: DATA COLLECTION

Once OPTIMAS is properly configured, you may begin collecting data from your images.

Capture

Once you have configured a camera or scanner acquisition source, simple tools let you capture images with a mouse click or a keystroke. You can even automate image acquisition with macros.

Enhance

Once an image has been acquired, some degree of image enhancement is typically required to facilitate the identification of image features. OPTIMAS provides many image enhancement techniques to correct for problems that prevent detection of image features.

Identify

OPTIMAS makes it easy to identify the features you want to measure in your image. You can use tools that draw, trace, or even automatically recognize features.

Measure

Next, determine the measurements you want to extract from your image. Since OPTIMAS includes many application macros for a variety of common applications, it is a good idea to verify whether there is a macro available to suit your requirements before you begin analyzing images.

Measurements, along with data layout and labeling specifications, are stored by OPTIMAS as measurement sets. You may select a set from those included with OPTIMAS, or create your own sets.

Step 3: REPORTING and MANAGING RESULTS

Once information has been extracted from your images, you can use OPTIMAS to communicate your results. OPTIMAS makes it easy to export results to various destinations for analysis; destinations may include an Excel spreadsheet, OPTIMAS binary data file, ASCII data file, or a custom report created using Microsoft Visual Basic, Access or Word.

Automation

Once the image analysis process has been defined, automation will ensure consistent results, ease of use, and repeatability. Automation can be achieved by recording screen activity or by high level custom programming using the Analytical Language for Images (ALI).

OPTIMAS Image Analysis Tools

Acquire

OPTIMAS is designed to make data acquisition simple. Its seamless interface supports virtually any image acquisition source, including analog and digital frame grabbers, scanners, PhotoCD, VCRs, or any other video source. Since many frame grabbers offer special functionality and configuration options, OPTIMAS can be configured to control frame grabber-specific behavior. OPTIMAS supports multiple cameras, such as standard video, high speed, digital, low light, and non-standard cameras in color and black and white. OPTIMAS also supports standard as well as proprietary image file formats.

Multiple Document Interface

OPTIMAS' Multiple Document Interface (MDI) allows you to open and acquire images of different types. This means you can view and manipulate more than one image frame at a time. An image frame is the workspace in which you view and manipulate images. The frame is distinct from the image; you create a frame and then open an image into it. For "live" acquisition you can create a frame that is backed by a frame grabber. These images can have different calibrations, image formats and thresholds, yet can all be simultaneously displayed and processed. Although most of the time you will want a frame to contain just one image, you can also open multiple images into a single frame.

Image Types

OPTIMAS supports a wide variety of image types, including different bit depths:

Other Tools

OPTIMAS also has image acquisition tools that:

Enhance

If image enhancement is necessary, OPTIMAS provides a comprehensive set of image processing techniques in convenient menus and simple dialog boxes. These procedures are macro-recordable and can be customized to suit your requirements.

Contrast

Enhancement can begin at the moment of acquisition; OPTIMAS can control the contrast and brightness of live images as they are acquired. You can control contrast and brightness by clicking on the Contrast tool to adjust contrast and brightness by dragging the mouse, or you can use the Camera Controls dialog box to manually set the levels of contrast and brightness.

ROI Selection

Image processing techniques are applied to Regions of Interest (ROI) in your image. You may define ROIs in terms of pixels or specified calibration units (although the default choice is pixels). ROIs may be drawn freehand, or be rectangular, circular, or full screen. Alternately, you can precisely define, save, and position the location of your ROIs. If an image has multiple ROIs, you may specify whether the image enhancement techniques are applied within individual ROIs or in overlapping areas.

Pseudo Color

Pseudo color lets you change the appearance of a monochrome image by reassigning luminance values. Pseudo color changes only the appearance of the image. Another way to apply pseudo color is to color a range of the histogram; this method permits you to color only the part of the image that corresponds to the features you want to enhance. You may create your own pseudo color models, or you may use several predefined pseudo color ramps:

Geometry

OPTIMAS' geometric functions let you control your image's scale, alignment, and rotation.

Scaling lets a source image be scaled to fit a destination image. Starting at the image's upper left pixel, the image can be scaled to fill the full size of the destination image, scaled to keep its aspect, or scaled to the destination image's X or Y axis.   Alignment lets you align a source image to a target image using a set of fiducial points that you specify.

Rotation provides extensive tools for image and ROI rotations and manipulation. The image data within the ROI may be rotated to any angle, either clockwise or counter-clockwise. You can specify the center of rotation to be any point within the active image window by manually marking the point or by supplying its spatial coordinates. Additional tools for flipping or reflecting the ROI about horizontal, vertical, or diagonal axes are also provided.

Filter

OPTIMAS filters are generally mathematical functions known as convolution masks that assign gray values to pixels based on the gray values of neighboring pixels. Other filters generate a value for the target pixel by sampling the neighbors and applying some other mathematical function to them

Average (3x3 and 5x5) These convolutions are designed to remove noise. The 5x5 average kernel's effects are stronger than the 3x3.

Gaussian (3x3 and 5x5) These convolutions are designed to perform a normalization by applying a discrete approximation of a Gaussian curve. Gaussians offer better performance in the frequency domain than standard averaging.

Median (3x3, 5x5 and 7x7) This filter replaces the target pixel with the median luminance of the neighboring pixels. 

TrimmedMean (3x3 and 5x5) This filter creates a histogram of neighboring pixels' luminance, removes the upper and lower 10% of the histogram, and replaces the target pixel with the mean of the remaining luminance values.

SharpenLow This convolution is a high-pass 3x3 filter that gently enhances detail.

SharpenMed This convolution is a high-pass 3x3 filter that sharpens edges and enhances detail.

SharpenHigh This convolution is a high-pass 5x5 filter that sharpens edges and enhances detail. This filter causes extreme magnification of gradients along edges in any direction. 

Laplace (medium and strong) These are 3x3 convolution filters that highlight edges in any direction. All pixels not identified as edges are changed to black; edges become white.

Sobel This filter highlights edges using the sum of the gradient in the X direction with the gradient in the Y direction, using two 3x3 convolution filters and computing the square root of the sum of the squares.

RobertsEdge This filter highlights edges in any direction by finding the square root of the sum of the squares of pixel differences in the two diagonal directions. The RobertsEdge operator uses a 2x2 filter and thus offsets the resultant pixels left and up by one pixel location.

HorizontalEdge This filter highlights horizontal edges (gradients) within the image. All pixels not part of horizontal gradients are changed to black; all edges become white.

VerticalEdge This filter highlights vertical edges (gradients) within the image. Pixels that are not identified as part of a gradient are set to black.

ColorFilter This filter strips the 3.58 MHz. color sub carrier from a color television signal. Some subtle blurring of the image may result.

StopMotion This filter stops jittering in a visual frame caused by movement. The StopMotion filter removes jitter by removing one of the interlaced video fields and filling in the missing lines of information by interpolating the precedent and antecedent lines.

2DFFT A two dimensional Fast Fourier transform to filter images between the spatial and frequency domains.

HDC/Wallis A set of specialized large kernel filtering functions. These filters are especially useful for large scale image smoothing, determining the locations of large regional gradients, and enhancing contrast to reveal faint crevices or similar features.

Compass Filter This filter runs a series of 3x3 edge convolutions at eight compass points, then copies them together using the Max operator to achieve a reconstruction of the edges.

Custom Filters OPTIMAS allows you to design your own single-pass or multi-pass custom convolution filters. You can declare kernel sizes and shapes and provide kernel coefficients.

Binary Morphology

Binary morphology works on two grayscale levels, black and white. OPTIMAS supports a variety of binary
image morphology operations

Invert Selecting this option will cause all foreground pixels to become background pixels and vice-versa.

Erode This function first segments a grayscale image into foreground and background components. In the resulting binary image, foreground pixels that are 8-connected to a background pixel are eliminated.

Conditional Erode This is an Erode function that is performed within marker and mask intensity boundaries that you specify.

Dilate Dilation is the reverse of Erosion. After segmenting a grayscale image into a binary image, the dilate operation identifies background pixels that are 8-connected to a foreground pixel and changes them to foreground. Finally, the dilated bitmap is copied back to the frame grabber.

Conditional Dilate This is a Dilate function that is performed within marker and mask intensity boundaries that you specify.

Open An erosion followed by a dilation.

Close A dilation followed by an erosion.

Outline In this operation, foreground pixels that are 4-connected to a background pixel remain unchanged. All other pixels change to background. This produces an 8-connected foreground boundary.

Fill This function fills holes in foreground regions. It first thresholds a grayscale image into foreground and background components, and then fills the holes in the foreground regions. Finally, the outline bitmap is copied back to the screen.

Skeletonize1 After segmenting the image into foreground and background components, this operation finds an approximate centerline for each connected component in the foreground. The centerline is found by repeatedly "eating away" at the boundaries of the component.

Skeletonize2 This is the same as Skeletonize1 except that a different algorithm is used to erode the boundaries. Skeletonize2 is better at preserving connectivity at locations on the image where line segments join together.

Thin This operation applies a morphological hit-or-miss transform to the foreground until there is no further change. The effect of thinning is to identify a set of pixels that describe underlying shapes on the image.
Prune Pruning may be applied to reduce the size of whiskers and hairs that are produced with either thinning or skeletonization. Because this operation very aggressively eats away foreground component boundaries, the number of pruning iterations desired can be specified.

Thicken1 Each thickening iteration fills in small holes and cracks on the boundary of foreground components. After several thickening iterations each component tends to approach the shape of an enclosing octagon.

Thicken2 Like Thicken1 except that after several thickening iterations each component approaches the shape of a more gently enclosing convex hull shape.

Watershed Separate This function separates touching blobs (connected areas of foreground pixels) by applying a watershed algorithm to an intermediate image derived from the foreground components of the segmented image. The intermediate image is formed by inverting the distance transform of the binary image.

Distance Transform This function labels each foreground pixel of an input binary image with the distance to its nearest background pixel.

Separate This macro allows you to separate connected areas of foreground pixels by performing a series of binary erosion passes. This utility works best on images with circular blobs touching at small junctions, such as dumb-bell shaped objects.

Grayscale Morphology

Grayscale morphology uses similar principles as binary morphology but operates on a gray image instead of a binary image. A probe (also known as a grid or kernel) can be defined and applied to a region of interest.

Probe elements correspond to pixels in the image; probe size can be specified to 3x3, 5x5, 7x7, 9x9, or 11x11 pixels. You can customize its shape, or choose from the following predefined probe shapes:

Once you've defined the probe, you may use it to apply any of the following functions:

Erode This operation replaces each pixel with the gray value of the darkest pixel in its neighborhood as defined by the shape of the probe. It removes light bridges between dark objects.

Dilate This operation replaces each pixel with the gray value of the brightest pixel in its neighborhood as defined by the shape of the probe. It removes dark bridges between light objects.

Top Hat This function subtracts the result of an opening from the original image. It is particularly useful for enhancing detail in an image in the presence of shading. 

Well This function subtracts the original image from the results of a closing of that image. This is
the inverse of a top hat operator.

Arithmetic Operations

Arithmetic operations let you combine images arithmetically on a pixel by pixel basis to reduce image noise, correct uneven lighting conditions, or subtract the background from an image. OPTIMAS provides many standard and complex operators:

You may wish to experiment with these operators and with combinations of filters and arithmetic
operations.

Identify

Feature identification is possible once the image has been enhanced. Identification techniques identify the foreground features you want to measure through image thresholding. Identified features of interest can then be marked by OPTIMAS' Feature Identification Tools.

Thresholding

Thresholding allows you to define the foreground of the image by selecting a range of gray values or colors - all other values are regarded as background. A group of connected pixels whose values are within the foreground is defined as a foreground feature.

Grayscale There are several techniques you can use to set the grayscale threshold range; you can use the mouse to select a range, type bounding values, scroll and drag to select values, or manually sample the image. Of these techniques, the most convenient may be the mouse method. The advantage of this method is that you can set the foreground while interacting with the image instead of the controls.

Color If you are working with a color image, you can select the foreground color for each signal band (e.g. RGB, HSL). Color thresholds are set by selecting regions of the histograms, typing specific threshold values, or sampling the image. Sampling lets you manually select colors in the image and set thresholds based upon those values.

Adaptive Since an uneven background can make it difficult to set a single grayscale threshold value which isolates foreground objects over the whole ROI, OPTIMAS provides Global or Local Smoothing tools that allow you to correct the luminance in images with smoothly varying backgrounds.

Multiple OPTIMAS lets you create multiple thresholds for color as well as grayscale images. Multiple thresholds operate the same way for both types of images, except that you will be prompted to select colors when working with color images. When you are using multiple thresholds, OPTIMAS automatically runs functions such as the Auto Areas tool once for each enabled threshold.

Automatic You can threshold images automatically to ensure repeatability. You have the options of unimodal thresholding, thresholding on light or dark objects, or on a specific number of phases. Each option supports multiple thresholding methods, such as the watershed technique that creates boundaries between the objects without changing the objects themselves, allowing automatic feature identification. Other methods include exponential fit, search for minimum, trim from endpoints, or minimize segment variance.

Edge Detection

OPTIMAS' edge detection markers can find edges or patterns with sub-pixel accuracy. Measurements can then be extracted based on the position, sampling index, or distance of edges detected in the image.

Feature Identification Tools

Feature identification tools within OPTIMAS let you identify the specific features within your image from which you want to extract measurements. The identification of these areas can be done manually with the following tools:

Alternately, features can be identified automatically with OPTIMAS' set of Automatic Tools. The Automatic Tools rely on the threshold tools described above, and you may specify sampling parameters for each tool.

Auto Lines tool automatically creates lines on all foreground objects as though the objects themselves had been skeletonized to a width of one pixel. You may set sampling parameters such as minimum length.

Auto Areas tool automatically creates areas based on the current threshold. You may set parameters such as boundary placement and minimum boundary length. You can also specify the inclusion or exclusion of blob holes, as well as the removal of areas touching the ROI border.

Auto Points tool automatically creates a set of points based on the current threshold. You may specify sampling parameters such as minimum point size.

Identification tools can be used in conjunction with morphology and filtering tools to separate objects from the background. Each identified area is treated as a distinct screen object and can be selected, moved, edited, and/or deleted.

Measure

Once features have been identified, you can begin extracting data from images.

Measurement Sets

First specify which measurements you want OPTIMAS to calculate and extract. These measurements, along with data layout and labeling specifications, are stored as measurement sets. You can create your own sets or select from the predefined sets included with OPTIMAS. Predefined measurement sets include:

You may set data sampling parameters for each measurement set. Such parameters include luminance limits for data extraction, area boundary intervals, point luminance averaging, line interval numbers, and line averaging width.

Application Macros

For more common applications, you may use one of several application macros already included with OPTIMAS. Application macros include:

Other application macros include:

Since OPTIMAS 6.0 is a full 32 bit application, pixel operations such as filters and measurements are 30-40% faster than previous 16 bit versions of OPTIMAS. OPTIMAS also supports direct access to OPTIMAS images from an external DLL.

Data Export

Typically, you will want to export your data for further analysis. You may specify an export destination for extracted data; you can send data to an Excel spreadsheet, OPTIMAS binary data file, ASCII data file, or a custom report created using Microsoft Visual Basic.

Measurement Explorer

The Measurement Explorer allows you to extract and view data for individually selected measurements.

You can interactively learn about each measurement and the data it generates. You can also view measurement data in the Viewbox, which provides feedback as you extract measurements. Viewboxes are dynamically linked to OPTIMAS measurements by Image HyperLinkTM. Each time you extract data, the Viewbox is automatically updated.

Automate

Once the image analysis process has been defined, automation will ensure consistent results, ease of use, and repeatability. The Analytical Language for Images (ALI) is the macro control language for OPTIMAS. Among its many features, ALI offers:

ALI also features large sets of built-in functions for:

Recording Macros

ALI macros can be created by recording screen activity or by custom programming.

When you create a macro by recording screen activity, OPTIMAS records your actions by writing a program (i.e., a macro) in the ALI programming language; you have the option of seeing this program being written. OPTIMAS 6.0 is compatible with OPTIMAS 5.2 macros.

A program created with the Macro Record facility will normally consist of a simple series of function calls interspersed with occasional variable assignments. Many useful tasks can be accomplished with this sort of program, but you will likely want to modify your simple recorded macros to correct mistakes made during recording, to add instructions for the user, to get information from the user, to check for errors, to compute and display intermediate results, etc. Because ALI is actually a full-featured programming language, all of these goals and many more may be accomplished:

Custom Programming

For custom programming, ALI has a syntax similar to the C language. However, ALI is not an implementation of C; it is a different language with semantics based on ideas borrowed from C, APL, and the UNIX statistical language S. ALI differs from C and other traditional computer languages in two major respects:

Run-time versions OPTIMAS (Optimate) are available for the distribution of custom macros.

Technical Summary

Hardware and operating system requirements

Use the following guidelines for configuring your OPTIMAS system:

An IBM-compatible personal computer equipped with at least a 80486DX microprocessor. We recommend Pentium CPU and PCI-bus peripherals for best performance. A graphics card that supports at least 256 colors. We recommend a high color, high resolution graphics card. 16 Mbytes of system memory (RAM). If you plan to work with image sequences or large images, we recommend 32 Mbytes or more. Color super-VGA monitor. Microsoft Windows 95 or Windows NT. Mouse or other pointing device supported by Microsoft Windows. CD to read the OPTIMAS distribution disk. At least one parallel port for the software key. A frame grabber compatible with OPTIMAS if you intend to acquire live images from video or digital cameras.