Watershed segmentation

The Plant Computer Vision (PlantCV) software package, is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. 

PlantCV v2 is the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning.

PlantCV is composed of modular functions in order to be applicable to a variety of plant types and imaging systems. PlantCV currently supports the analysis of standard RGB color images (aka "VIS"), standard grayscale images (e.g. near-infrared, "NIR"), thermal infrared images, grayscale images from chlorophyll fluorescence imaging systems ("PSII"), and hyperspectral ("ENVI") images. 

PlantSeg is a tool for cell instance aware segmentation in densely packed 3D volumetric images. The pipeline uses a two stages segmentation strategy (Neural Network + Segmentation). The pipeline is tuned for plant cell tissue acquired with confocal and light sheet microscopy. Pre-trained models are provided.

The macro will segment nuclei and separate clustered nuclei in a 3D image using a 2D Gaussian blur, followed by Thresholding, 2D hole filling and a 2D watershed. As a result an index-mask image is written for each input image.

This workflow processes images of cells with discernible nuclei and outputs a binary mask containing where nuclei are detected.

The macro will segment nuclei and separate clustered nuclei in a 3D image using a distance transform watershed. As a result an index-mask image is written for each input image.

Performs watershed algotirhm with ij-1.52i.jar. legacy:ij.plugin.filter.EDM("watershed").

The macro will segment nuclei and separate clustered nuclei using a binary watershed. As a result an index-mask image is written for each input image.

The interactive Watershed Fiji plugin provides an interactive way to explore local maxima and threshold values while a resulting label map is updated on the fly.

After the user has found a reliable parameter configuration, it is possible to apply the same parameters to other images in a headless mode, for example via ImageJ macro scripting.