This node is used to process cell and nuclei binaries. Cells without nucleus are removed. Only the largest nucleus in every cell is kept. Both binaries are given IDs so the cell and its nucleus has the same ID. This node processes binaries for the use in Cell measurement.

Removes binary objects which cannot contain a circle of the specified Radius.

Figure 943. Example

Figure 944. 

Removes small holes from the current binary image.

Fills all holes in the binary image which would be destroyed by Erosion of the specified parameters.

Figure 945. Example

Morphological closing performs Dilation followed by Erosion (see Binary processing > Morphology > Close). Close operation is restricted by Parent (Ref) binary and will connect only children in same parent. Node can be used, for example, to connect multiple nuclei in cell.

Fills in holes in binary image.

Figure 946. Example

If the connected binary does not contain any binary information, it is fully filled.

Inverts the binary image to its negative.

Removes binary pixels on the image border.

Separates binary objects into multiple smaller objects. The higher the Count, the fewer objects will be separated.

Figure 947. Example

Morpho Separate Objects dialog box appears.

Figure 948. 

Separates bright objects from the background based on both the binary layer and the intensity. The object detection can be adjusted using the Peak strength parameter.

Separates dark objects from the background based on both the binary layer and the intensity. The object detection can be adjusted using the Peak strength parameter.

Smooths binary image contours.

Figure 949. Example

Figure 950. 

It is possible to define the strength of the function by specifying the number of iterations.

Smooths the contours of each binary object present in the connected binary image and correctly separates objects from one another.

Morphological opening performs Erosion followed by Dilation. This has the effect of clearing all objects which are too small with respect to the specified parameters. Larger structures are not significantly affected.

Figure 951. Example

Morphological closing performs Dilation followed by Erosion. It fills all holes in objects which are too small with respect to the specified parameters. Closely neighboring objects get connected.

Figure 952. Example

Figure 953. 

Erosion sets each pixel to the value computed as minimum from all pixels in the matrix. Erosion shrinks objects and removes small ones. A non-convex object can split into several parts.

Figure 954. Example

See Also 
Binary > Dilate

Dilation sets each pixel to the value computed as maximum from all pixels in the matrix. Dilation expands objects and structures in binary image. Neighboring objects are connected and small holes are filled.

Figure 955. Example

Performs Erosion followed by Dilation. This has the effect of clearing all objects which are too small with respect to the specified parameters. Larger structures are not significantly affected.

Morphological closing performs Dilation followed by Erosion. It fills all holes in objects which are too small with respect to the specified parameters. Closely neighboring objects get connected.

Euclidean erosion sets each pixel to the value computed as minimum from all pixels within the specified Radius. This will shrink/remove binary objects in the image.

Euclidean dilation sets each pixel to the value computed as maximum from all pixels within the specified Radius. This will enlarge/merge binary objects in the image.

Binary processing > Circular Morphology > Circular Clean

Removes binary objects that cannot contain a sphere of the specified radius i.e. objects smaller than Radius.

Binary processing > Circular Morphology > Circular Close Holes

Fills all holes which cannot contain a sphere of the specified radius i.e. holes smaller than Radius.

Performs morphologic opening on binary image using the selected linear matrix. The choice of the structuring element defines the direction in which the binary image will be affected. See Binary > Open.

Figure 956. Example

Binary processing > Linear morphology > Linear Open Z

Highlights structures with a linear pattern using the Linear Open function in the Z direction. Please see Image Processing > Linear Morphology > Linear Open.

Performs morphologic closing on binary image using the selected linear matrix. The choice of the structuring element defines the direction in which the binary image will be affected. See Binary > Close.

Figure 957. Example

Binary processing > Linear morphology > Linear Close Z

Highlights structures with a linear pattern using the Linear Close function in the Z direction. Please see Image Processing > Linear Morphology > Linear Close.

Performs erosion on binary image, it removes a layer of pixels in the direction of the selected matrix. See Binary > Erode.

Figure 958. Example

Binary processing > Linear morphology > Linear Erode Z

Highlights structures with a linear pattern using the Linear Erode function in the Z direction. Please see Image Processing > Linear Morphology > Linear Erode.

Performs dilation on binary image, it adds a layer of pixels in the direction of the selected matrix.

See also Binary > Dilate.

Figure 959. Example

See Also 
Binary > Erode

Binary processing > Linear morphology > Linear Dilate Z

Highlights structures with a linear pattern using the Linear Dilate function in the Z direction. Please see Image Processing > Linear Morphology > Linear Dilate.

Calculates and marks the centroid of the objects found in the connected binary image.

Converts binary objects to their centroids. Select the method of determining centroid positions.

Figure 960. 

Connects binary objects which are closer to each other than the specified distance by a thin line.

Figure 961. 

Transforms binary image to its 1 pixel wide contour.

Figure 962. Example

This function expands non-convex binary image objects to their convex boundaries.

Figure 963. Example

Displays the distance of each object pixel to the nearest boundary (background pixel) as an intensity value.

The resulting color image is a floating point image.

Figure 964. Example

See Also 
Binary > Advanced Morphology > Ultimate Erosion

Creates a binary layer with marked geodesic centers.

A Geodesic Center is a point within the object, such that the sum of its distances to every other point in the object is minimal. For convex objects, it corresponds to the center of gravity. The algorithm used to calculate the geodesic center is approximate.

Figure 965. Illustration showing the geodesic center of the green object.

Note

This operation on one object may result into multiple centers. Same as if the you use the Binary processing > Morphology > Open node using the matrix.

Figure 966. 

Grey

Original object.

Green

The object after Binary processing > Morphology > Open.

Red

Geodesic centers.

This command creates the granulometry image from the binary image.

Marks objects with homotopic marks. Homotopic marking is a sequential homotopic thinning. It is used for marking objects. A filled object (with no holes) is transformed to a single point. Every hole leaves a closed contour.

Figure 967. Example

Replaces each binary object by its inscribed circle. Inscribed circle is the largest circle that can be contained in the object.

Figure 968. 

This command creates medial axis from the current binary objects.

Figure 969. 

Sequentially removes end-points from binary image. This function is used for simplification of skeletons by removal of branches. Closed contours remain unchanged.

Figure 970. Example

The following window appears.

Figure 971. 

Sequentially erodes binary image, but leaves small areas which would completely disappear in the next erosion.

Figure 972. Example

Grows each binary object by a circle (2D) / sphere (3D) of the specified radius. Objects will not be merged together.

Grows objects in the image until the fraction of the total object area to total image area is the same as the parameter Area Fraction.

Grows each binary object up to the specified intensity. Objects will not be merged together.

Creates zones of influence by the fast 'pipe of pixels' algorithm. Performing this command, the ZonesOfInfluenceFast is called and appended to the list of executed functions.

For every binary object in the connected image this node creates an interior circle object and a ring around it. It is guaranteed that the corresponding circle and ring have the same object ID and that the number of circles and rings is equal. Places where the circle or ring should disappear both are removed in order to maintain the same IDs. In case it breaks in more objects the biggest is taken and the others are discarded. Circle can be restricted by adding a hole (width of the hole is measured from the circle boarder). Adjust the width of each feature by typing a value into the edit box or using the arrows.

Figure 973. Features of the Circle & Ring node.

Dilates a binary image without changing the number of objects - it is a homotopical transformation.

Figure 974. Example

Performs the watershed (flooding) algorithm starting from the connected binary objects. The flooding is based on image pixel intensities.

Creates zones of influence by drawing four connectivity borders. For each pixel of a border between two objects, the distance to both objects is the same.

Figure 975. Example

Makes a skeleton of the binary image. The current binary image is dilated and then intersected by reference binary image. This step is repeated until there is no difference in the sequence of consecutive images. The current image may be e.g. erosion of the original image copied to reference binary image. In this case the function reconstructs only bigger objects and rejects smaller objects.

Skeleton is a representation that largely preserves extent and connectivity of original binary objects, while pruning most of original pixels.

Figure 976. Example

Binary processing > Skeleton > Skeleton

Makes a skeleton of the 3D binary image.

See Also 
Binary > Skeleton Morphology > Skeletonize

The purpose of this command is to create 1pixel seeds out of a skeletonized binary image. This function serves for automatic recognition of the intersection points of single-pixel lines.

Figure 977. Example

Purpose of this command is to create 1pixel seeds out of a skeletonized binary image. It preserves only ending points of the skeleton and clears all other pixels.

On a skeletonized binary image, branches (free endings) shorter than the defined length will be removed.

Figure 978. Example

Figure 979. 

On a skeletonized image, connects branch peaks. Set sensitivity of the function in the window:

Figure 980. 

On a skeletonized image, circular (enclosed) objects with equivalent diameter (eqdia) smaller than the defined value will be deleted. You do not necessarily want to delete small objects but the ones which are mis-detected.

Figure 981. 

Shortens each branch of a skeleton by the specified amount in pixels.

This operation adds pixels to ensure that each pixel which is only 8-connected will become 4-connected. 4-connected pixels are those connected vertically or horizontally, but not diagonally.

See Connectivity.

This operation removes pixels to ensure that each pixel which is only 4-connected will become 8-connected. 8-connected pixels are those either vertically, horizontally, or diagonally. See Connectivity.

Filters the connected objects using a filter or multiple filters. Set a filter by selecting the filtering Feature, setting the Comparator and the numeral Value. To add another filter, click New Filter.... To delete a filter, click .

Binary processing > Filter objects > Filter Objects

Removes any binary object not meeting the specified condition. Objects having the specified feature outside of the specified range will be removed.

Selects binary objects from the binary image which are present in input table.

Selects the number of largest objects specified in the Count of objects box.

Deletes binary objects from the binary image which are present in input table.

Removes binary objects touching the image border.

Removes binary objects touching the image frame. Select units and set the width, height and position of the frame. Then choose a mode defining which objects are to be removed.

Draws a circle to the binary image.

Draws an Ellipse to the binary image.

Adds a rectangle frame to the connected binary layer. Define the frame by entering the distance from the image borders (Left, Top, Right, Bottom) with proper Units. Define whether the frame will be Filled or not and set the visualization Mode determining the interaction between the binary layer and the new frame.

Draws a line to the binary image.

Draws a line connecting centers of two largest binary objects in connected binary layer.

Draws multiple parallel lines to the binary image.

Draws a rectangle to the binary image.

Draws a grid of markers to the binary image.

Draws one marker to the binary image.

Insert arbitrary number of circles from a table into the binary image.

Insert arbitrary number of fixed circles from a table into the binary image.

Insert arbitrary number of lines from a table into the binary image.

Binary processing > Insert objects > Plane

Draws a plane to the binary volume. Plane is defined by coordinates of any point belonging to it, by the space orientation and mode of interacting with the existing layer. Select Units and Coordinates of any point belonging to the plane. Then choose a parallel Plane and Mode for interacting with the existing layer.

This function displays the binary objects in several different colors. The algorithm is optimized so that two neighboring objects would never display in similar colors.

Assigns any color to any value of the selected source column used from the connected table.

In the following scenario, positive and negative numerical values are divided into two intervals labeled with different colors. Therefore all negative values will be assigned a red color and all positive values will be assigned a green color.

Figure 982. Positive and negative values divided into two intervals by number 0.

Figure 983. Example of the color assignment defined above.

If you have deleted some objects , this command renumbers all the remaining ones so that numbering starts from 1 and is continuous. Only numbers greater than the deleted ones are renumbered.

Renumberes the binary objects with values taken from the the connected table. Source column has to be specified.

Renumbers the binary objects of detected TMA (Tissue microarray) cores. Select Skip Gaps to not include numbers of missing cores in indexing (so binaries will be numbered from 1 to N, where N is count of binary objects).

This node automatically detects columns and rows of grid of TMA cores. Then it assigns new indexes so that cores in the upper row have smaller indexes than cores in the lower row. In each row, cores are indexed from left to right. Numbers of missing cores are not used (for example, if you have 10 cores in a 4x3 grid, core in the bottom right corner (if not missing) will always have an index of 12).

For good results cores should be laying in a square grid which is not rotated.

Figure 984. TMA Renumbering Example (before)

Figure 985. TMA Renumbering Example (after)

Adds a border to the binary layer. Define the Top, Left, Right and Bottom borders and set the Value which will be added to the borders.

Crops the connected binary image to the specified width and height [px]. Shift the cropping rectangle by filling the Start x and Start y position.

Crops to the center of the connected binary image by the specified width and height [px].

Shrinks the source binary image so that it fits into a square with a given size. Aspect ratio of the binary is maintained as well as all other properties.

Flips the source binary horizontal and/or vertical. Simply check Horizontal flip or Vertical flip to flip the image in the particular direction.

Binary processing > Transformations > Flip

Flips the source 3D binary horizontal and/or vertical. Simply check Horizontal flip or Vertical flip to flip the image in the particular direction.

Shifts the connected binary image horizontally (X Offset) or vertically (Y Offset). Area shifted outside the image borders can be placed to the opposite side if Rotate values around image borders is checked.

Resizes the binary image by a given ratio.

Resizes the source binary image (A) so that it has the same size as the reference binary image (B).

Rotates the connected binary image by the specified angle.

Binary processing > Connect slices > Connect

Connects 2D slices from subsequent frames into the 3D binary. Most of the time, it is not necessary to use this node. All 3D binary processing nodes do this operation automatically. Use it only when the final binary result is 2D and is desired to be 3D. This node does not change the underlying binary data.

Binary processing > Connect slices > Connect Cells

Connects 2D slices from subsequent frames which have the largest overlap into 3D binary object.