Scientific Volume Imaging

• You can create an ROI of the region you want to remove. Either by selecting a region with the selection tool or by using objects and anchors from the menu ROI -> Set.
• When an ROI is created, go to ROI -> Dataset cropping -> Remove intensties inside ROI and then export the image using the first button in the Data section at the left. Choose any setting, e.g. Overwrite All
• Visualize the new image with the Surface Renderer.
• When you have chosen Append to original, you can do some nice transitions in the Movie Maker, by increasing the transparency of one channel and decreasing the other the cut-off will be slowly displayed. Just play with it and explore all its possibilities.

If you do not have the Object Analyzer, you can use the cp command to copy a part of an image in Huygens Professional. Replicate the image with the repl command and clear the new image, such that it only contains zero values. Then copy any arbitrary block of your image to this new image with the cp command. For example, if you want the cut of a 100x100x100 image in the x-direction, with the left part deleted:

a cp -> b -dest {50 0 0 0 0} -src {50 0 0 0 0} -span {50 100 100 0 0}

In the Operations window of Huygens Professional, go to Manipulation->Copy&Stretch. The Tcl command cs is used as follows:

a cs -> b -fromzero

There are two limits:

• The labels are saved in a 16 bits signed integer format. So 2¹⁵ - 1 = 32767 is the maximum number of labels (objects). That seems to be very much, but is not enough in the case of a very noisy image where a large amount of objects will be detected. Filtering or deconvolution is the solution here.
• The size of objects is limited to 10⁵ pixels. In case this limit is reached, try to use a higher threshold to make the object smaller.

Using a GUI You can directly crop an image using the crop tool, which can be found in the buttonbar, and under Edit -> Crop. Or with the shortcut Alt-B. At startup the Intelligent Cropper estimates the region you will need for deconvolution. Read more at Intelligent Cropper. In Essential the cropper is found under Tools -> Crop...

Using scripting - crop symmetrically In case you want to crop using the Tcl Shell or within a script you can do that by removing a symmetrical border. Use the add/remove border command, border. The size of the border to be removed can be specified as an option of this command, for instance '-100 -120 -40' to cut away 50 pixels in X on each side, 60 in Y and 20 in Z. Type border ? to see how the command is used.

Using scripting - crop any part In case you want an arbitrary part of an image you should first create a destination image which has the exact same size as that part. There are two methods to do this:

1. One way is to use the repl command to create a copy and use add/remove border to trim it down to the desired size. In this way the microscopic and other parameters of the destination are surely correct.
2. The other way is to create a new image with the desired size and then call for the adopt command to copy the microscopic parameters from the original to the cropped image.

After the new image is created, then use the cp command (see Operations windows under Arithmetic->Copy image part) on the original and specify source, destination and span as follows:

• source: the coordinates of the origin of the region you want to take out.
• destination: leave at 0 0 0 0 0(default)
• span: set to a size as large or larger than the sizes of the destination image. For instance if the destination has size {100 100 40} span could be {100 100 100 1 1}. The last '1' (ones) are for the (not used) time and channel dimensions.

CoLocalization analysis gives information about the amount of spatial overlap between structures in different data channels, for 3D images and time series.

1. map your 3D or 4D multichannel image to a 2D multichannel 2D image, for instance with the slice or miniMIP commands.
2. use chan2rgba to map that image to a 2D packed 4channel (RGBA) image of bytes. The chan2rgba command has a lot of options to manipulate contrast, colour and gamma. Syntax:
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   source chan2rgba -> dest

3. use the -type tiffrgb option to the save command, for example:
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   foo save foo.tif -type tiffrgb

Deconvolve first: Thresholding noisy images is tricky since voxels from noise can be seen as objects. It's a good idea first to deconvolve your image and then find a threshold which excludes all background.

Proceed with the analysis:

1. Directly label a source image using labelDirect. Syntax:
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   source labelDirect -> <destination> <threshold> <seedThreshold>
   ?-frame <frame>? ?-chan <channel>? ?-garb <volume>?

2. Do the actual analyzing: Select the ORIGINAL image and use 'Analyze labeled image' to get information about the numbered objects.
   It calculates the total volume (number of voxels in the objects, the minimum and maximum value in the objects, the sum of all voxels in the objects and the location of their Center of Mass).
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   yourImage labelDirect -> b 100 150
   yourImage analyze b

In short, this algorithm minimizes the intra-class (between classes) variance of the classes object and background. This algorithm is equal to maximizing the inter-class (within class) variances. For more information, check e.g. Wikipedia

The "seed" value is not calculated by Otsu, but can dependent on its calculated value. This is dependent on whether checkbox at the right of the seed parameter is checked or not.

Unchecked: the relative seed refers to the image maximum, with the "threshold" as the minimum value.