Galaxy Image Analysis is mainly developed by the Biomedical Computer Vision (BMCV) Group at Heidelberg University. If you would like to contribute, please see our contribution instructions.
Galaxy Image Analysis is part of the Image Community in Galaxy.
If Galaxy Image Analysis helped with the analysis of your data, please do not forget to cite: https://doi.org/10.1016/j.jbiotec.2017.07.019
- Apply anisotropic diffusion with MedPy
- Apply a morphological operation with SciPy
- Concatenate images or channels
- Filter 2D image with scikit-image
- Perform color decomposition
- Perform histogram equalization with scikit-image
- Permutate image axes
- Process images using arithmetic expressions with NumPy
- Remove image background with scikit-image
- Scale image with scikit-image
- Show image info with Bioformats
- Slice image into patches
- Switch axis coordinates
- Convert binary image to label map
- Convert binary image to points (center of masses)
- Convert binary image to points (point coordinates)
- Convert image format with Bioformats
- Convert label map to binary image with NumPy
- Convert label map to points (center of masses)
- Convert point coordinates to label map
- Convert single-channel to multi-channel image with NumPy
- Convert to OME-Zarr with Bioformats
- Compute Voronoi tessellation with scikit-image
- Count objects in label map
- Extract 2D features with scikit-image
- Filter label map by rules
- Merge neighbors in label map
- Perform 2D spot detection
- Perform segmentation in densely packed 3-D volumetric images with PlantSeg
- Perform segmentation using region-based fitting of overlapping ellipses with RFOVE
- Perform segmentation using deformable shape models with SuperDSM
- Split label map using morphological operators
- Threshold image with scikit-image
- Compute image orientation with OrientationPy
- Perform affine image registration (intensity-based)
- Perform affine image registration (landmark-based)
- Performs projective transformation with/without labels
- Performs projective transformation
- Colorize labels with NetworkX
- Compute image segmentation and object detection performance measures with SegMetrics
- Evaluate segmentation with EvaluateSegmentation
- Overlay images
- Visualize detections
- Compute image features with Mahotas
- Extract top view with OpenSlide
- Perform curve fitting
- Unzip
This work has been supported by the BMBF-funded Heidelberg Center for Human Bioinformatics (HD-HuB) within the German Network for Bioinformatics Infrastructure (de.NBI) #031A537C.
