ZEISS Knowledge Base

The following operations are part of the pipeline.

Denoising

Set of operations performing noise reduction. Choose the more suitable method based on your sample and define the diameter.

Denoising parameters

Parameter		Description
Channels		Sets the processing and analysis target channel(s).
Method		Sets the denoising algorithm.
–	Bilateral	The Bilateral filtering can reduce the noise in an image while maintaining edges. A bilateral filtering blurs an image using both domain and range neighborhoods.
–	Discrete Gaussian	The Discrete Gaussian filter blurs an image by convolution with a discrete Gaussian kernel. This method is fast, but blurs edges.
–	Flow-driven	The curvature Flow-driven denoising filter is an anisotropic diffusion method used to reduce noise or unwanted detail in images while preserving specific image features.
–	Mean	The Mean filter blurs the image by calculating a new intensity value for each pixel. The new intensity is equal to the average of the intensity values of the pixels in the local neighborhood.
–	Median	The Median filter sets the intensity for each pixel to the median of the intensity values in the local neighborhood. The median is the intensity value and the center of the ordered sequence of all pixels in the local neighborhood.
–	Particle enhancement	The Particle Enhancement Filter can be used to extract bright structures of a certain size from a noisy background. It convolves a given image with a special restoration kernel.
Diameter		Sets the reference objects the reference objects diameter. Note: The filter size is expressed as the smaller objects' diameter of the structures that you want to preserve or enhance. This parameter must be expressed in metric unit. Note: You can measure the diameter directly from the dataset with the Measurement tool.

Allows the segments detection using one of the available automatic threshold methods.

By default, the Method: Auto is selected. Depending on the selection, different parameters are available.

Parameter		Description
Channel		Sets the processing and analysis target channel.
Method		Sets the threshold algorithm. By default, Auto is selected.
–	Simple	This method uses a single threshold and segments everything below or above it.
–	Range	This method uses two Thresholds to define the range of intensities to segment. Everything outside of this range is neglected.
–	Percentile	This method uses a single threshold and segments all intensities below or above it. The threshold is given as a percentile and is based on the data.
–	Percentile range	This method uses two thresholds to define the range of intensities to segment. Everything outside of this range is neglected. The thresholds are given as percentiles and are based on the data.
–	Auto	This method uses a single threshold and segments all intensities below or above it. The threshold is automatically calculated based on the data.
–	Adaptive mean	This method uses local information around each pixel to set a threshold in relation to the local background. It is based on Niblack's thresholding method.
–	Mean	This method uses local information around each pixel to set a threshold in relation to the local background.
Object type		Sets whether to select light or dark objects, i.e., whether the threshold value should be below or above.
–	Bright	Selects the bright objects. This is good for images with a dark background.
–	Dark	Selects the dark objects. This is good for images with a bright background.
Thresholder		Sets the auto algorithm to be used.
–	Otsu	The algorithm maximizes the inter-class variance.
–	Huang	The fuzzing threshold algorithm using the Shannon's entropy function.
–	Li	The algorithm minimizes the cross-entropy.
–	Yen	The algorithm is a 2-factor criterion-based automatic multilevel thresholding.
Range		Sets the extent of the input data for the automatic threshold calculation.
–	Plane	For each plane of the image set an individual threshold is calculated.
–	Time point	For each time point of the image set an individual threshold is calculated.
–	Image set	For the whole image set only one threshold is calculated.

Object Feature Filter

The previously detected objects are filtered by the selected feature (e.g., Volume). The used criteria can be set from the dropdown list. Multiple features can be set.

Object Feature Filter parameters

Parameter		Description
Input		Sets the filter input. If more than one segment operation is present in your pipeline, the correct input source must be set.
Is of type		Sets the type of filter which filters the objects based on the selected type. By default, it is set on Any for all object types. The list of available features is updated accordingly.
Filter selection		Sets the function for the segmentation is performed.
Criterion Settings		These parameters depend on the selection of the filter.
+ Add Filter		Allows to add a Simple, Ratio or Tag filter criterions.

Compartmentalization

The detected objects' positions are compared to verify its partial or total overlap conditions.

Compartmentalization parameters

Parameter		Description
Inputs		Sets the reference tag (first field) and the subject tag (second field). The subject segments are compared to the reference segments to verify if they match the criteria of compartmentalization.
Overlap method		Sets the compartmentalization criteria. When you click on the icon, a new dialog appears.
–	Inside	The subject objects must be completely included into the reference objects
–	Intersecting	The subject objects must be included into the reference objects for the overlap percentage set.
–	Close	The distance between the subject objects and the reference objects must be lower than the max distance value.
+ Add input		Adds another hierarchical level.
Hierarchy		Sets the hierarchy level of the input.
Output		Sets the compartmentalization output. According to the compartmentalization criteria setup, each compartment result can be stored independently.