7.1 LUT
7.2 Threshold
7.3 Image statistics
7.4 Binary to ROI
7.5 Polygon selection
7.6 Phasor plot
7.7 Generating QF-Pro maps
7.1. LUT
“LUT” stands for “Lookup Table,” serving as a mechanism in image analysis to map pixel intensity values from the original image to corresponding display colours. It allows users to adjust and enhance the visual representation of images by assigning specific colours or gradients to different intensity levels, facilitating improved interpretation and analysis.
LUT Plot
The adjustment function of LUT values can be accessed in the software after acquiring images, in the “Experiment results” window, by pressing “LUT”. The same LUT adjustments can be applied to multiple images simultaneously, allowing users to choose the images to which the adjustments will be applied. Users have the flexibility to adjust various Look-Up Table (LUT) values to customize image visualization according to their preferences and analytical needs. This can be done in multiple ways:
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Direct Input: Users can manually input different LUT values into the appropriate fields, enabling precise control over image display parameters.
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Graph Interaction: Alternatively, users can interact with the LUT graph by clicking and dragging the upper or lower limits, allowing for intuitive adjustment of image contrast and brightness.
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Crop: zooms in when the area delimited between the lower and upper limits is too small.
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Reset: If needed, users have the option to revert to the original LUT settings with a single click, restoring the default image visualization settings.
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Color Scale Selection: The system provides a drop-down menu for users to select different color scales for LUT visualization. By default, intensity images are displayed in greyscale, while lifetime images are presented in rainbow scale.
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Intensity Modulated Display (IMD): Users can toggle the IMD button to merge the intensity and lifetime channels of corresponding result images. This feature is enabled by default, providing enhanced visualization of image data.
The typical LUT ranges for different image types are as follows:
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Intensity Images: 0.0 – 0.15 (arbitrary units)
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Lifetime Images: 0.5 – 3.5 nanoseconds
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QF-Pro® Maps: 0 – 40%
LUT applied to images
7.2 Threshold
The QF-Pro® software incorporates a threshold function designed to enhance image clarity and facilitate efficient analysis, in the “Experiment results” window, by pressing “Threshold”. This feature automatically applies a threshold mask to the selected image, effectively separating signal from background noise.
Threshold histogram
Users have the flexibility to adjust the threshold manually by setting absolute intensity values, using the sliding scale bar, “+” and “-” buttons for fine-tuning, or a text box for manually entering threshold values. Alternatively, they can opt for automatic thresholding, which is recommended in most cases for optimal results. In addition to threshold adjustment, the software provides tools for image de-noising, eliminating speckles of background noise to further improve image clarity. This ensures accurate analysis and interpretation of imaging data.
Threshold applied to images
7.3 Image statistics
Image statistics are fundamental tools in visual data analysis, applied to the regions of interest (ROIs) previously delineated. These ROIs may represent individual binary ROIs within an image, or a whole image (depending on whether the user is imaging cells or tissues). The following statistical parameters are calculated for each ROI:
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Mean: The mean is the average of all pixel values in the image. It is calculated by summing all pixel values and dividing by the total number of pixels.
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Median: The median is the value that occupies the central position when pixel values are sorted from lowest to highest. It is less sensitive to extreme values than the mean.
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Maximum: The maximum value is the brightest pixel in the image, i.e., the highest value among all pixel values.
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Minimum: The minimum value is the darkest pixel in the image, i.e., the lowest value among all pixel values.
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Standard Deviation: The standard deviation measures the dispersion of pixel values around the mean. It provides a measure of how uniformly values are distributed around the mean.
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First Quartile: The first quartile is the value found in the ordered data that leaves 25% of pixel data values behind.
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Third Quartile: The third quartile is the value found in the ordered data that leaves 75% of pixel data values behind.
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IQR (Interquartile Range): The interquartile range is the difference between the third quartile and the first quartile. It is a measure of dispersion that removes the influence of extreme values.
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Upper Whisker: In a box plot, the upper whisker represents the maximum extension allowed for normal values before they are considered outliers. It is calculated as the third quartile plus 1.5 times the interquartile range (IQR). Any value above this limit is considered an outlier.
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Lower Whisker: On the other hand, the lower whisker represents the maximum extension allowed for normal values below the first quartile before they are considered outliers. It is calculated as the first quartile minus 1.5 times the interquartile range (IQR). Any value below this limit is considered an outlier.
These values can be viewed within the statistics window of the QF-Pro® software or exported directly to Microsoft Excel with the option to change its name and directory.
7.4 Binary to ROI
The “Binary to ROI” function is designed to be used with cell images, separating individual cells into separate ROIs. This allows for independent analysis or processing. When performing thresholding (see 4.2) on cell image, a binary image is generated, distinguishing certain areas as foreground (selected) and others as background (not selected). Subsequently, the “Binary to ROI” option is utilized to segregate these cells into distinct regions within the image. Each region is then assigned a unique identifier or label (a number), facilitating individualized statistical analysis at a later stage.
Images after applying binary to ROI
7.5 Polygon selection
The Polygon Selection feature, accessible within the “Experiment results” window, enabels users to delineate specific areas of interest within images by drawing polygons. This functionality facilitates targeted analysis and precise examination of selected regions.
Example of a created polygon
Usage:
.1. Accessing Polygon Selection: Users navigate to the “Experiment results” window and locate the “Polygon Selection tool”.
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Drawing Polygons: Upon selecting the tool, users can draw polygons directly onto the image. They can click to create each vertex of the polygon, defining the desired shape.
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Completing the Selection: After defining the polygon, users connect the final vertex to the starting point, thereby closing the shape. There is a “Delete” option in case the polygon needs to be redone.
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Analyzing Selected Area: Once the polygon is closed and the button “Ok” pressed, users can perform various analyses specific to the selected region, such as measuring intensity, fluorescence, or other parameters
7.6 Phasor plot
A phasor plot serves as a powerful graphical tool in signal processing and analysis, offering insights into the phase relationship between two signals or two components within a signal. In essence, it represents complex numbers in a two-dimensional space, with the real part represented along one axis and the imaginary part along the other. In frequency-domain FLIM (Fluorescence Lifetime Imaging Microscopy), a phasor plot aids users in analyzing the lifetime of fluorescent signals captured in an image. Here’s a breakdown of how it works and how users can interpret it:
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Understanding the Phasor Plot: The phasor plot graphically represents each pixel in the image, using parameters known as “G” and “S” instead of traditional x and y axes. These parameters capture the phase and modulation of the fluorescent signal emitted by each pixel, respectively.
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Interpreting the Plot: Each point on the phasor plot corresponds to a specific pixel in the image, with its position determined by the lifetime characteristics of the fluorescence emitted by that pixel. Pixels with similar lifetimes cluster together on the plot. Robust signals form smaller circle-shaped clusters near the line of the phasor plot, indicating a strong signal. Weaker signals typically yield more dispersed cloud-shaped clusters. Pixels located furthest from the phasor plot line usually originate from background noise.
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Advanced Analysis: Advanced users can directly interact with the phasor plot to threshold the associated lifetime image, selectively highlighting pixels of interest while excluding background noise. This technique is particularly useful for separating weaker signals from noise. However, in most cases, traditional thresholding methods suffice for analysis purposes.
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Creating the Phasor Plot: To create it, users must go to the “Experiment results” window, and press the “Phasor Plot” button. There is also the button “Apply” to apply the selected points as threshold.
Phasor Plot
7.7 Generating QF-Pro maps
QF-Pro® maps provide a visual representation of the functionality of the protein of interest within a Region of Interest (ROI). These maps are generated by calculating the efficiency of energy transfer from the donor chromophore to the acceptor chromophore. The resulting FRET Efficiency, also known as the QF-Pro® score, reflects the functional state of the protein being studied.
QF-Pro® maps are generated automatically upon acquisition of a donor and donor-acceptor sample pair. When the thresholding of the donor acceptor image is altered, the QF-Pro® map must be re-calculated. To do this, the user should click the “Re-calculate FRET” button. To create QF-Pro® maps, the software first calculates the average lifetime of all donor pixels within the sample. Then, for each pixel in the donor-acceptor image, the following calculation is performed, where “t{DA}” is the lifetime of the donor in the presence of the acceptor, and “t´{D}” is the average of all donor-only lifetime values obtained from all pixels of the donor-only sample:
QF-Pro map image (right)
Recalculate button
FRET efficiency formula
Go to next section: 8.- Experiment, data and image saving