Module: MeasureImageQuality

Measure Image Quality measures features that indicate image quality.
This module can collect measurements indicative of possible image abberations, e.g. blur (poor focus), intensity, saturation (i.e., the percentage of pixels in the image that are minimal and maximal). Details and guidance for each of these measures is provided in the settings help.

Please note that for best results, this module should be applied to the original raw images, as opposed to images that have already been corrected for illumination.

Available measurements



Calculate metrics for which images?

This option lets you choose which images will have quality metrics calculated.

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?

Select Yes to add the image's rescaling value as a quality control metric. This value is set only for images that loaded using the Input modules. This is useful in confirming that all images are rescaled by the same value, since some acquisition device vendors may output this value differently. See NamesAndTypes for more information.

Calculate blur metrics?

Select Yes to compute a series of blur metrics. The blur metrics are the following, along with recomendations on their use:


Spatial scale for blur measurements

(Used only if blur measurements are to be calculated)
The LocalFocusScore is measured within an N × N 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 additional scales for each image.

Calculate saturation metrics?

Select Yes to calculate the saturation metrics PercentMaximal and PercentMinimal, i.e., the percentage of pixels at the upper or lower limit of each individual image.

For this calculation, 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, both these measures 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?

Select Yes to 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.

Calculate thresholds?

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)
Select Yes to calculate thresholds using all the available methods. Only the global methods are used.
While most methods are straightfoward, some methods have additional parameters that require special handling: See the IdentifyPrimaryObjects module for more information on thresholding methods.

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. Only the global methods are applied. 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)
Select Two classes if the grayscale levels are readily distinguishable into foregound (i.e., objects) and background. Select Three classes 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 classes is used)
Choose whether you want the middle grayscale intensities to be assigned to the foreground pixels or the background pixels.