Measure Image Quality
measures features that indicate image quality, including measurements of blur (poor focus), intensity, saturation (i.e., the percentage of pixels in the image that are minimal and maximal)
Please note that for best results, this module should be applied to the original raw images, as opposed to images that already been corrected for illumination.
- Blur metrics
- FocusScore: A measure of the intensity variance across the image.
- LocalFocusScore: A measure of the intensity variance between image sub-regions.
- Correlation: A measure of the correlation of the image for a given spatial scale.
- PowerLogLogSlope: The slope of the image log-log power spectrum.
- Saturation metrics
- PercentMaximal: Percent of pixels at the maximum intensity value of the image.
- PercentMinimal: Percent of pixels at the minimum intensity value of the image.
- Intensity metrics
- TotalIntensity: Sum of all pixel intensity values.
- MeanIntensity, MedianIntensity: Mean and median of pixel intensity values.
- StdIntensity, MADIntensity: Standard deviation and median absolute deviation (MAD) of pixel intensity values.
- MinIntensity, MaxIntensity: Minimum and maximum of pixel intensity values.
- TotalArea: Number of pixels measured.
- Threshold metrics:
- Threshold:: The automatically calculated threshold for each image for the thresholding method of choice.
Calculate metrics for which images?
This option lets you choose which images will have quality metrics calculated.
- All loaded images: Use all images loaded with LoadImages or LoadData modules
(images loaded with LoadSingleImage are excluded). The quality metrics selected below will be
applied to all these images.
- Select...: Select the desired images from a list. The quality metric settings selected
will be applied to all these images. Additional lists can be added with separate settings.
Select the images to measure
(Used only if Select... is chosen for selecting images)
Choose one or more images from this list. You can select multiple images by clicking
using the shift or command keys. In addition to loaded images, the list includes
the images that were created by prior modules.
Include the image rescaling value?
Checking this setting adds the image's rescaling
value as a quality control metric. This value is set only for images
that loaded using LoadImages or LoadData. This is useful in confirming
thqat all images are rescaled by the same value, since some acquisition
device vendors may output this value differently.
See LoadImages for more information.
Calculate blur metrics?
Check this setting to compute a series of blur metrics. The blur metrics are the
following, along with recomendations on their use:
- Power Spectrum Slope: The power spectrum contains the frequency information
of the image, and the slope gives a measure of image blur. A higher slope indicates
more lower frequency components, and hence more blur (Field, 1997). This metric is
recommended for blur detection in most cases.
- Correlation: This is a measure of the image spatial intensity distribution
computed across sub-regions of an image for a given spatial scale (Haralick, 1973).
If an image is blurred, the correlation between neighboring pixels becomes high,
producing a high correlation value. A similar approach was found to give optimal
performance for fluorescence microscopy applications (Vollath, 1987).
Some care is required in selecting an appropriate spatial scale because differences
in the spatial scale capture various features: moderate scales capture the
blurring of intracellular features better than small scales and larger scales
are more likely to reflect intercellular confluence than focal blur. A spatial scale
no bigger than the feature of interest is recommended, although you can select as
many scales as desired.
- Focus Score: This score is calculated using a normalized variance,
which was the best-ranking algorithm for brightfield, phase contrast, and DIC images (Sun, 2004).
Higher focus scores correspond to lower bluriness.
More specifically, the focus score computes the intensity variance of the entire
image divided by mean image intensity. Since it is tailored for autofocusing
applications (difference focus for the same field of view), it assumes that the
overall intensity and the number of objects in the image is constant, making it less
useful for comparision images of different fields of view. For distinguishing
extremely blurry images, however, it performs well.
- Local Focus Score: A local version of the Focus Score, it subdivides the
image into non-overlapping tiles, computes the normalized variance for each, and
takes the mean of these values as the final metric. It is potentially more useful
for comparing focus between images of different fields of view, but is subject
to the same caveats as the Focus Score. It can be useful in differentiating good versus
badly segmented images in the cases when badly segmented images usually contain no cell
objects with high background noise.
- Field DJ (1997) "Relations between the statistics of natural
images and the response properties of cortical cells," Journal of the Optical
Society of America. A, Optics, image science, and vision
- Haralick RM (1979) "Statistical and structural approaches to texture,"
Proc. IEEE, 67(5):786-804.
- Vollath D (1987) "Automatic focusing by correlative methods," Journal of Microscopy
- Sun Y, Duthaler S, Nelson B (2004) "Autofocusing in Computer Microscopy:
Selecting the optimal focus algorithm," Microscopy Research and
Spatial scale for blur measurements
(Used only if blur measurements are to be calculated)
The Local Focus Score is measured within an NxN pixel window applied to the image,
whereas the Correlation of a pixel is measured with repsect to its neighbors
N pixels away.
A higher number for the window size measures larger patterns of
image blur whereas smaller numbers measure more localized patterns of
blur. We suggest selecting a window size that is on the order of the feature of interest
(e.g., the object diameter). You can measure these metrics for multiple window sizes
by selecting adiditonal scales for each image.
Calculate saturation metrics?
Checking this option calculates maximal and minimal percentages
as saturation metrics. The percentage of pixels at
the upper or lower limit of each individual image is
calculated. The hard limits of 0 and 1 are not used because images often
have undergone some kind of transformation such that no pixels
ever reach the absolute maximum or minimum of the image format. Given
the noise typical in images, this should be a low percentage but if the
images were saturated during imaging, a higher than usual
PercentMaximal will be observed, and if there are no objects, the
PercentMinimal value will increase.
Calculate intensity metrics?
Checking this option will calculate image-based
intensity measures, namely the mean, maximum, minimum, standard deviation
and median absolute deviation of pixel intensities. These measures
are identical to those calculated by MeasureImageIntensity.
Automatically calculate a suggested
threshold for each image. One indicator of image quality is that these threshold
values lie within a typical range.
Outlier images with high or low thresholds often contain artifacts.
Use all thresholding methods?
(Used only if image thresholds are calculcated)
Calculate thresholds using all the available methods. Only the global methods
While most methods are straightfoward, some methods have additional
parameters that require special handling:
- Otsu: Thresholds for all combinations of class number, minimzation
parameter and middle class assignment are computed.
- Mixture of Gaussians (MoG): Thresholds for image coverage fractions
of 0.05, 0.25, 0.75 and 0.95 are computed.
Select a thresholding method
(Used only if particular thresholds are to be calculated)
This setting allows you to apply automatic thresholding
methods used in the Identify modules.
For more help on thresholding, see the Identify modules.
Typical fraction of the image covered by objects
(Used only if threshold are calculated and MoG thresholding is chosen)
Enter the approximate fraction of the typical image in the set
that is covered by objects.
Two-class or three-class thresholding?
(Used only if thresholds are calculcated and the Otsu thresholding method is used)
if the grayscale levels are readily distinguishable into foregound
(i.e., objects) and background. Select Three
if there is a
middle set of grayscale levels that belongs to neither the
foreground nor background.
For example, three-class thresholding may
be useful for images in which you have nuclear staining along with a
low-intensity non-specific cell staining. Where two-class thresholding
might incorrectly assign this intemediate staining to the nuclei
objects, three-class thresholding allows you to assign it to the
foreground or background as desired. However, in extreme cases where either
there are almost no objects or the entire field of view is covered with
objects, three-class thresholding may perform worse than two-class.
Assign pixels in the middle intensity class to the foreground or the background?
(Used only if thresholds are calculcated and the Otsu thresholding method with three-class thresholding is used)
Choose whether you want the middle grayscale intensities to be assigned
to the foreground pixels or the background pixels.