|Notable features include:||
|QIA-64 Workflow Steps
|Adjustments are typically performed on images to correct acquisition problems
(such as nonuniform illumination) or to convert an as-acquired image to a different
color space in which the important subjects are more readily discriminated. These
adjustments are generally performed before the enhancements described below.
|Multiple images may be combined for various purposes:
with one or more other images. The stored image is not altered and remains in the disk
file until replaced.
|Some processing operations are most straightforwardly applied in Fourier or
frequency space rather than in the pixel domain. This space is particularly well suited
to the removal of periodic noise (or conversely isolating just a periodic structure from
random noise), and for finding matched or partially matched objects and shapes in a
|There are many different algorithms that are applied to images in the spatial or pixel
domain that deal with image noise, detail extraction (such as points, edges and lines),
and conversion of one type of image to another that may be more suitable for
thresholding and measurement (e.g., converting texture to brightness). The routines in
this category are in addition to such built-in Photoshop Filter tools as the Gaussian Blur,
Unsharp Mask, Median Filter, and Minimum and Maximum (which perform erosion and
dilation, using a round or square neighborhood). QIA's additions are provided in two
|The thresholding tool in Photoshop simply divides pixels into black (foreground) or
white (background) based on a user-adjusted value that is marked on the brightness
histogram. The result is a binary (black and white) image in which pixels are either "on"
The functions in QIA-64 have greater flexibility, including the use of color information.
In most cases, image processing using the previous QIA-64 steps are required to
prepare an image so that it can be successfully thresholded. The purpose is to assure
that the pixels of interest (the foreground) have the same values wherever they happen
to lie in the image, and that the values are unique and different from the pixels in the
Thresholding is rarely perfect for every pixel, and the Binary Processing operations
(shown in the next section) may be needed after thresholding.
|Operations performed on binary images are often necessary to clean up imperfect
delineation of features and structures by thresholding. They are also important for
isolating the points and lines that are most directly useful for subsequent measurement.
These functions assume that the image consists only of black and white images.
Any pixel that is not exactly white (value 255) and consequently part of the background,
is treated as being black, or part of a feature. This allows features that have a range of
brightness or color values to be measured.
|These functions operate on the entire image area, or on a rectangular ROI that has
been created. They are in the following categories:
|A “feature” is defined in these functions as a group of contiguous non-white pixels
that are completely surrounded by white background. Pixels are defined as touching
and hence part of the same feature if they touch along sides or at corners (often
called 8-connectivity). Depending on the nature of the image, features may represent
objects, groups of objects, or other structures.