Morphological operation

Morphological image processing
Mathematical morphology




DeepCell is neural network library for single cell analysis, written in Python and built using TensorFlow and Keras.

DeepCell aids in biological analysis by automatically segmenting and classifying cells in optical microscopy images. This framework consumes raw images and provides uniquely annotated files as an output.

The jupyter session in the read docs are broken, but the one from the GitHub are functional (see usage example )


3D Fill holes (mcib3d)


Runs fill holes operation on 3D images.

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Maximum (3D) VIB Protocol


This component convolves the image with maximum filter. Each voxel is set to the maximum value of its neighborhood. The neighborhood is defined by a kernel, which has a diameter of 3 voxels.

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3D ImageJ Suite


This suite provides plugins to enhance 3D capabilities of ImageJ.

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h-Dome transformation, useful for spot detection.

Jython code example:

from de.unihalle.informatik.MiToBo.core.datatypes.images import MTBImage
from de.unihalle.informatik.MiToBo.morphology import HDomeTransform3D
from ij import IJ

imp = IJ.getImage()
mtb = MTBImage.createMTBImage( imp.duplicate() )
hdome = HDomeTransform3D(mtb, 10.0)
mtbdone = hdome.getResultImage()
imp2 = mtbdone.getImagePlus()



"The plugin analyzes fluorescence microscopy images of neurites and nuclei of dissociated cultured neurons. Given user-defined thresholds, the plugin counts neuronal nuclei, and traces and measures neurite length."[...]" NeuriteTracer is a fast simple-to-use ImageJ plugin for the analysis of outgrowth in two-dimensional fluorescence microscopy images of neuronal cultures. The plugin performed well on images from three different types of neurons with distinct morphologies."

This plugin requires parameter setting: Threshold levels and scale (see more details on the related publication)