ZEISS Knowledge Base

Difference in Job Mode

The available functionality of the individual steps can differ between the wizard in Free Mode and the steps that can be added in Job Mode, e.g. the Interactive checkbox is not visible in Job Mode.

In this step you can select the segmentation method that is applied and set parameters for the segmentation of the objects that you want to measure. All the objects detected with the current settings are highlighted in the image. Note that during the setup of the analysis (via Setup Image Analysis in Free Mode), the segmentation is only performed on the area visible in the viewport. If you enter the interactive analysis or are in Job Mode, the image will be fully segmented.

Parameter		Description
Execute		Activated: This step is included when the analysis is run. Otherwise the step is skipped.
Interactive		Activated: The segmentation can be changed interactively while the analysis setting is run.
Class List		Selects the class for which you want to define the segmentation. You can specify different settings for each class.
Segmentation Method		Displays and selects a segmentation method for the currently selected class with the dropdown.
-	Explore	Opens the dialog to explore and select an available segmentation method, see Segmentation Method Selection Dialog.

The visible parameters depend on the selected segmentation method. The following parameters sections can be available:

Sharpen Section

Parameter		Description
Sharpen		Select how to improve the sharpness by enhancing contrast at fine structures and edges of the image before the threshold values are set. The following methods are available:
-	None	No sharpening algorithm is applied.
-	Delineate	Applies the Delineate method. It emphasizes edges around structures in an image, which is useful for images where the gray value range of structures differs clearly from the gray value range of the pixels around them.
-	Unsharp Masking	Applies the Unsharp Masking method.
Threshold		Only visible, if you have selected Delineate. Sets the threshold value for edge detection. The threshold value should correspond roughly to the gray value difference between objects and the background.
Size		Only visible, if you have selected Delineate. Sets the size of the edge detection filter, i.e. the size of image details which are enhanced. The smaller the Size value is, the finer are the details affected by the tool. The value should correspond to the size of the transition area between objects and the background.
Strength		Only visible, if you have selected Unsharp Masking. Sets the strength of the Unsharp Masking. The higher the value selected, the greater the extent to which small structures are enhanced.

Here you can define the threshold values for the selected class in the class list.

Parameter		Description
Threshold		Sets the brightness boundaries between which pixels are considered.
–	Reset	Clears the upper and lower thresholds. No pixels are considered.
–	Undo	Undoes the last change made to the threshold values.
–	Redo	Restores the last undone change to the threshold values.
Color Model		Only visible if the image is a color image, see Color Model.
-	RGB	In RGB mode you can define the threshold values for the red, green and blue color channels.
-	HLS	In HLS mode you can define the threshold values for hue, saturation and lightness.
–	Low	Sets the lower threshold. Only pixel values above this value are considered. The range of possible values depends on the bit depth of the image.
–	High	Sets the upper threshold. Only pixel values below this value are considered. The range of possible values depends on the bit depth of the image.
–	Invert	Only pixels outside the threshold boundaries are considered, i.e. those pixels below the lower threshold and above the higher threshold.
–	Full Range	Sets the lower threshold to 0 and the upper threshold to the highest value (depending on bit depth). The entire range of pixel values is considered.
Histogram		In the histogram you can change the lower and upper threshold value for the activated value. Drag the lower or upper adjustment handle or shift the entire highlighted area between the lower and upper threshold value.
Click		Click in the image on the regions that you want to define as objects. The threshold values are adapted according to the pixel intensities at the clicked position in the image.
Automatic		The threshold values are determined automatically from the histogram. During setup only the part of the image displayed in the viewport is taken for the calculation of the threshold. After the automatic calculation of the threshold values you can further modify the threshold values found interactively by selecting Click for threshold value definition.
Pick Behavior		Only visible, if you have selected Click.
-	+	Enables you to expand the currently segmented regions by the gray values/colors of the objects subsequently clicked on.
-	-	Enables you to reduce the currently segmented regions by the gray values/colors of the objects subsequently clicked on.
Tolerance		Only visible, if you have selected Click. Specifies how many additional pixel values are included in the selection based on their brightness. A higher value means that more pixel values similar to the selected one are included. A lower value means that only the exact pixel value selected is included.
Neighborhood		Only visible, if you have selected Click. Specifies how many additional pixel values are included in the selection based on their physical proximity to the selected pixel. A higher value means that more pixels surrounding the selected pixel are included. The threshold boundaries are adapted so that all the pixel values of these neighboring pixels are included. A lower value means that the boundaries are adapted based on only the pixels directly next to the selected pixel.
Method		Only visible, if you have selected Automatic. Selects the algorithm that is used to automatically detect the threshold boundaries. The most suitable algorithm depends on your precise requirements. The value also depends on the bit depth of the image. The following methods are available:
-	Otsu	The pixel values below the threshold are designated as background and those above the threshold as foreground. It iterates through all possible threshold values and calculates the variance of the pixel intensities of the background and foreground pixels for each value. The threshold is set at the value that minimizes both variances. This method is particularly suited to light objects on a dark background.
-	Maximum Peak	Separates background and foreground pixels at the maximum value of the histogram.
-	Iso-Data	The pixel values below the threshold are designated as background and those above the threshold as foreground. An initial threshold value is chosen, and the mean pixel intensity of the foreground and background pixels is calculated. These two mean values are averaged and the result serves as the input threshold for the next calculation. The process is repeated until the threshold value no longer changes.
-	Triangle Threshold	The algorithm constructs a line between the peak of the highest frequency pixel intensity and the lowest pixel intensity. The distance between the line and the histogram is computed for all values along the line. The pixel intensity where the line is longest is used as the threshold. This method is particularly suited when the foreground pixels only have a weak peak in the histogram.
-	Three Sigma Threshold	Calculates the pixel value that occurs most frequently. The standard deviation of the values in the peak is calculated. The threshold is set to the pixel intensity that is the sum of the average peak value and three times the standard deviation.

Color Model

RGB

Here you can set the RGB channel threshold values.

Parameter	Description
Red	Activates the red channel in the Expander Histogram.
Green	Activates the green channel in the Expander Histogram.
Blue	Activates the blue channel in the Expander Histogram.

HLS

Here you can set the hue, lightness and saturation threshold values.

Parameter	Description
Hue	Activates the hue in the Expander Histogram.
Lightness	Activates the lightness in the Expander Histogram.
Saturation	Activates the saturation in the Expander Histogram.

Variance Section

This section is only visible if Variance-Based Thresholding is selected.

Parameter		Description
Kernel Size		Sets the kernel size used to calculate the variance value of one pixel with its neighboring pixels.
Variance		Defines the lower and upper threshold for the variance.

Model Section

This section is only visible if AI Semantic Segmentation or AI Instance Segmentation is selected.

Parameter		Description
Model Name		Displays the name of the currently selected model.
–	Select Model	Opens the dialog to select a segmentation model. Note that you can only use models trained on a single channel.
Model Version		Only visible if AI Instance Segmentation is selected. Displays the currently selected model version.
Model Class		Displays the name of the currently used model class.
–	Reset	Resets/Deletes the selected model.
Min. Confidence		Sets the minimum value (in %) for the confidence that a certain pixel belongs to the segmented class. The default value is 51.

Select Model Dialog

Parameter	Description
Model Name	Selects a model. Only models trained on one channel images are shown here because only those can be used to segment a specific class assigned to a specific channel.
Model Class	Selects the model class.
Model Version	Only visible for AI Instance Segmentation. Selects the version of the model.
OK	Selects the respective model.
Cancel	Cancels the model selection and closes the dialog.

Subtract BG Section

Parameter		Description
Subtract BG		Only visible if Segmentation with Background Subtraction is selected. Selects which kind of background subtraction is performed.
-	None	No background subtraction is performed.
-	Rolling ball	The rolling ball background subtraction is performed.

Object Size & Hole Section

Parameter		Description
Min. Object Size		Sets the minimum size in pixels that an object must have in order to be segmented.
Min. Hole Size		Sets the minimum size in pixels that a hole must have in order to be recognized for segmentation. This input is synchronized with the input for Min. Object Size, which must not be smaller than Min. Hole Size.
Fill all Holes		Specifies how holes in detected objects are treated.
–	On	Fills holes in segmented objects ().
–	Off	Does not fill the holes in segmented objects ().

Binary Section

Parameter		Description
Binary		Selects which morphological operations are performed on the segmented (binary) image.
-	None	No operation is performed.
-	Open	Performs first erosion and then dilation. The effect is smoothing and removing of isolated pixels.
-	Close	Performs first dilation and then erosion. The effect is smoothing of the objects and filling of small holes.
-	Dilate	Enlarges the boundaries of segmented regions. Areas grow in size and holes within the regions become smaller.
-	Erode	Erodes boundaries of the segmented regions. The areas shrink in size and holes within the areas become larger.
Count		Sets how often the selected binary operation is performed with the slider or input field.

Separate Section

Parameter		Description
Separate		Selects whether you want to process the image further after segmentation. Objects that are touching one another can be separated using different methods.
-	None	Objects are not separated.
-	Morphology	Separates objects by first reducing and then enlarging them, making sure that once objects have been separated they do not merge together again.
-	Watersheds	Separates objects that are roughly the same shape. The result is two shapes separated by a thin 1-pixel boundary. The rest of the shape perimeter remains unchanged. This method may however result in the splitting of elongated objects.
Count		Sets the count value, which is similar to a Sigma for Gauss applied to a binary image.

Suppress Section

Parameter	Description
Suppress Invalid	Activated: Discards invalid pixels at the border of the image.
Suppress Border	Only visible if Variance-Based Thresholding is selected. Activated: Suppresses the border pixels which might be incorrect, as areas outside of the image are filled with zeros. The excluded area depends on the used kernel size.

Segmentation Method Selection Dialog

With this dialog you can select the method that is used for segmenting the class currently selected in the class list.

Parameter		Description
Global Thresholding		Applies a fixed threshold across the entire image to segment objects of interest. Use it for images with consistent object intensity like fluorescence images.
AI Instance Segmentation		Uses a deep learning model trained on arivis Cloud to distinguish and separate individual objects, even if touching or overlapping. Use it for precise identification in complex images. You need the Docker Desktop software running on your PC and have a suitable model available, see Machine Learning and AI Functionalities in ZEN core.
AI Semantic Segmentation		Uses a semantic machine learning model trained with Intellesis in ZEN core or arivis Cloud for classifying regions. Use it for labeling image regions by predefined classes, using random forest or deep learning. For semantic segmentation models, you need an installation of the 3rd party Python Tools.
Background Subtraction		Uses a rolling ball algorithm to subtract uneven background, then applies a global threshold to segment objects. Use it for images with varying background intensity.
Variance-Based Thresholding		Segments based on intensity changes. Use it for brightfield images where objects are distinguished by variance rather than consistent intensity.
Dynamic Thresholding		Applies local thresholds to different regions to handle inhomogeneous backgrounds. Use it for images with uneven illumination.
Don't show this automatically		Activated: The dialog is not opened automatically anymore for all users and can only be opened with the Explore button. Deactivated: The dialog opens automatically when entering the respective step of the image analysis.
OK		Uses the selected segmentation method for the class and closes the dialog.

Automatic Segmentation

See also