Synonyms
Image feature extraction

FishInspector

Description

The software FishInspector provides automatic feature detections in images of zebrafish embryos (body size, eye size, pigmentation). It is Matlab-based and provided as a Windows executable (no matlab installation needed).

The recent version requires images of a lateral position. It is important that the position is precise since deviation may confound with feature annotations. Images from any source can be used. However, depending on the image properties parameters may have to be adjusted. Furthermore, images obtained with normal microscope and not using an automated position system with embryos in glass capillaries require conversion using a KNIME workflow (the workflow is available as well). As a result of the analysis the software provides JSON files that contain the coordinates of the features. Coordinates are provided for eye, fish contour, notochord , otoliths, yolk sac, pericard and swimbladder. Furthermore, pigment cells in the notochord area are detected. Additional features can be manually annotated. It is the aim of the software to provide the coordinates, which may then be analysed subsequently to identify and quantify changes in the morphology of zebrafish embryos.

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Find Maxima (Python)

Description

 

Maxima finding algorithm recreated from implementation in Fiji(ImageJ)

This is a re-implementation of the java plugin written by Michael Schmid and Wayne Rasband for ImageJ. The original java code source can be found in: https://imagej.nih.gov/ij/developer/source/ij/plugin/filter/MaximumFinder.java.html 

This implementation remains faithful to the original implementation but is not 100% optimised. The java version is faster but this could be alleviated by compiling c code for parts of the code. This script is simply to provide the functionality of the ImageJ find maxima algorithm to individuals writing pure python script.

find maxima comparison.

ZEN Intellesis Trainable Segmentation

Description

Perform Advanced Image Segmentation and Processing across Microscopy Methods
 

Overcome the bottleneck of segmenting your Materials Science images and use ZEISS ZEN Intellesis, a module of the digital imaging software ZEISS ZEN.
Independent of the microscope you used to acquire your image data, the algorithm of ZEN Intellesis will provide you with a model for automated segmentation after training. Reuse the model on the same kind of data and beneft from consistent and repeatable segmentation, not influenced by the operator. 
ZEN Intellesis offers a straightforward, ease-to-use workflow that enables every microscope user to perform advanced segmentation tasks rapidly.

Highlights

  • Simple User Interface for Labelling and Training
  • Integration into ZEN Measurement Framework
  • Support for Multi-dimensional Datasets
  • Use powerful machine learning algorithms for pixel-based classifcation
  • Real Multi-Channel Feature Extraction
  • Engineered Feature Set and Deep Feature Extraction on GPU
  • IP-Function for creating masks an OAD-enabled for advanced automation
  • Powered by ZEN and Python3 using Anaconda Python Distribution
  • Just label objects, train your model and segment your images – there is no need for expert image analysis skills
  • Segment any kind of image data in 2D or 3D. Use data from light, electron, ion or x-ray microscopy, or your mobile phone
  • Speed up your segmentation task by built-in parallelization and GPU (graphics processing unit) acceleration
  • Increase tolerance to low signal-to-noise and artifact-ridden data
  • Seamless integration in ZEN framework and image analysis wizard
  • Data agnostic
  • Compatibility with 2D, 3D and up to 6D datasets
  • Export of multi-channel or labeled images
  • Exchange and sharing of models
  • GPU computing
  • Large data handling
  • Common and well-established machine learning algorithms
  • SW Trial License available

CEM

Description

Computer-assisted Evaluation of Myelin formation (CEM) is a collection designed to automate myelin quantification. It requires use input to choose the best threshold values. The myelin is calculated as an overlap between neuronal signal and oligodendrocyte signal. Results are given as pixel counts and percents.

CEM runs as an imageJ plugin with an optional Matlab extension to remove cell bodies. More details are published at Kerman et al. 2015 Development. Supplemental Material includes a detailed user manual and the download link.

Myelin

MaMuT

Description

MaMuT is an end user plugin that combines the BigDataViewer and TrackMate to provide an application that allow browsing, annotating and curating annotations for large image data.

Tensorflow

Description

"An open source machine learning framework for everyone "

TensorFlow™ is an open source software library for high performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. Originally developed by researchers and engineers from the Google Brain team within Google’s AI organization, it comes with strong support for machine learning and deep learning and the flexible numerical computation core is used across many other scientific domains.

has topic
TensorFlow

MIPAV

Description

The MIPAV (Medical Image Processing, Analysis, and Visualization) application enables quantitative analysis and visualization of medical images of numerous modalities such as PET, MRI, CT, or microscopy. Using MIPAV's standard user-interface and analysis tools, researchers at remote sites (via the internet) can easily share research data and analyses, thereby enhancing their ability to research, diagnose, monitor, and treat medical disorders.

wnd-charm

Description

WND-CHARM is a multi-purpose image classifier that can be applied to a wide variety of image classification tasks without modifications or fine-tuning, and yet provides classification accuracy comparable to state-of-the-art task-specific image classifiers. WND-CHARM can extract up to ~3,000 generic image descriptors (features) including polynomial decompositions, high contrast features, pixel statistics, and textures. These features are derived from the raw image, transforms of the image, and compound transforms of the image (transforms of transforms). The features are filtered and weighted depending on their effectiveness in discriminating between a set of predefined image classes (the training set). These features are then used to classify test images based on their similarity to the training classes. This classifier was tested on a wide variety of imaging problems including biological and medical image classification using several imaging modalities, face recognition, and other pattern recognition tasks. WND-CHARM is an acronym that stands for "Weighted Neighbor Distance using Compound Hierarchy of Algorithms Representing Morphology."

Generated features