Measuring object properties
Ultimately, most researchers want to extract numbers from their image data. These could correspond to fluorescent intensity across cells, or number of cells per colony, or density of filaments per region. NimbusImage allows you to make these computations easily by defining properties. A property (think: area) can be associated with an object and listed and exported for plotting and analysis. You can easily compute a lot of different properties using NimbusImage out of the box because of the flexibility that its tagging and connection system allows. For instance, if you want to find the count the number of spots connected to the basement membrane, that is easy to do with just a few clicks.
First, click on Object List, then, in "Properties", click the blue "Measure Objects" button:
That brings up the Property window:
Choose the tag of the object you want to quantify:
Then choose the Algorithm, like "Blob metrics". It will bring up a list of options:
Click "Create Property" and it will create and run the property worker. When done, it will look like this:
When you close the Property panel, it will open up the Properties pane. Click on "nucleus Blob metrics", then click on the Area checkbox:
Now your property will show up in the property list:
If you push "t", it will show the values in the image itself:
These values can be exported into a CSV under "Objects" -> Actions -> Export CSV
Blob metrics (area, perimeter, etc.)
The Blob Metrics property worker calculates a comprehensive set of morphological measurements for blob-shaped (polygon) objects in your dataset. This is particularly useful for analyzing cell shapes, nuclei, or any other blob-like structures you've annotated.
Available metrics:
Area: The total area enclosed by the object (in square pixels or physical units)
Perimeter: The length of the object's boundary (in pixels or physical units)
Centroid: The geometric center (x,y coordinates) of the object
Elongation: Measures how stretched out the object is (value between 0-1, where 1 is maximally elongated)
Convexity: Ratio of the object's area to the area of its convex hull (measures how convex vs. concave the shape is)
Solidity: Ratio of the object's perimeter to the perimeter of its convex hull
Rectangularity: How well the object fits within its minimum bounding rectangle
Circularity: How closely the object resembles a perfect circle (4π × Area/Perimeter²)
Eccentricity: Measures how much the object deviates from being circular (value between 0-1, where 0 is a circle)
How to use:
Create blob objects in your image (manually or using automated tools)
Tag these objects appropriately (e.g.,
nucleus,cell, etc.)Create a new property using the Blob Metrics worker
Select which tags to analyze
Choose whether to use physical units (μm, mm, etc.) or pixel units
Run the property worker to calculate metrics for all matching objects
Physical units:
When the "Use physical units" option is enabled, all measurements will be converted from pixels to the selected physical unit (μm, mm, m, or nm) based on the pixel size metadata in your image. This allows for consistent measurements across datasets with different magnifications or resolutions.
This property is useful for:
Measuring and comparing cell or organelle sizes
Analyzing shape changes in response to treatments
Quantifying morphological differences between cell types
Correlating shape features with biological function
Best practices for intensity measurements:
Background correction: Consider using background subtraction before measuring intensities for more accurate results
Consistent exposure: For comparative studies, ensure all images were acquired with the same exposure settings
Channel selection: Carefully select which channel to measure based on your experimental design
Annulus size: When using annular measurements, adjust the radius to match the biological structure you're analyzing (e.g., typical cytoplasm width)
Validation: Visually verify that your measurements align with the visible intensity patterns in your images
Blob Intensity
The Blob Intensity property worker calculates pixel intensity statistics inside blob-shaped (polygon) objects in your dataset. This is ideal for measuring fluorescence within cells, nuclei, or other structures you've annotated.
Available metrics:
Mean Intensity: The average pixel intensity within the object
Max Intensity: The brightest pixel value within the object
Min Intensity: The dimmest pixel value within the object
Median Intensity: The median pixel value (50th percentile)
25th Percentile Intensity: The intensity value below which 25% of pixels fall
75th Percentile Intensity: The intensity value below which 75% of pixels fall
Total Intensity: The sum of all pixel intensities within the object
How to use:
Create blob objects in your image (manually or using automated tools)
Tag these objects appropriately (e.g.,
nucleus,cell, etc.)Create a new property using the Blob Intensity worker
Select the channel you want to measure intensity from (this can be different from the layer where annotations are drawn)
Run the property worker to calculate intensity metrics for all matching objects
Applications:
Quantifying protein expression levels within cells
Measuring nuclear vs. cytoplasmic signal ratios
Comparing fluorescence intensities between experimental conditions
Identifying cells with high or low expression of a marker
Blob Intensity Percentile
This worker extends the basic intensity analysis by allowing you to specify exactly which percentile to measure, giving you more flexibility for your specific analysis needs.
Parameters:
Channel: The image channel to measure intensity from. Note that this can be different from the channel where annotations are drawn. So you can calculate, e.g., the RFP intensity in objects defined by the DAPI channel to calculate nuclear RFP intensity.
Percentile: A value between 0 and 99.99999 to specify which percentile intensity to calculate (default: 50)
Output:
Nth Percentile Intensity: The intensity value at your specified percentile
When to use:
When you need to focus on a specific portion of the intensity distribution
For filtering out outliers (using high or low percentiles)
When the median (50th percentile) doesn't fully capture the intensity characteristics you're interested in
Blob Annulus Intensity
The Blob Annulus Intensity worker measures pixel intensity in a ring-shaped region around each blob object. This is particularly valuable for quantifying cytoplasmic signals around nuclei or membrane markers surrounding cells.
Parameters:
Channel: The image channel to measure intensity from
Radius: The width of the annular region in pixels (default: 10)
Available metrics:
Mean Intensity: The average pixel intensity within the annular region
Max Intensity: The brightest pixel value within the annular region
Min Intensity: The dimmest pixel value within the annular region
Median Intensity: The median pixel value in the annular region
25th Percentile Intensity: The intensity value below which 25% of pixels fall
75th Percentile Intensity: The intensity value below which 75% of pixels fall
Total Intensity: The sum of all pixel intensities within the annular region
Applications:
Measuring cytoplasmic fluorescence around nuclear objects
Quantifying membrane-associated markers surrounding cells
Analyzing protein localization patterns at cell boundaries
Studying gradient distributions of signals around organelles
Blob Annulus Intensity Percentile
This worker combines the flexibility of percentile selection with annular region measurement, allowing for precise control over which statistical metric to use in your ring-shaped regions of interest.
Parameters:
Channel: The image channel to measure intensity from
Radius: The width of the annular region in pixels (default: 10)
Percentile: A value between 0 and 99.99999 to specify which percentile intensity to calculate (default: 50)
Output:
Nth Percentile Intensity: The intensity value at your specified percentile within the annular region
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