Image segmentation

This note presents the design of a scalable software package named ImagePy for analysing biological images. Our contribution is concentrated on facilitating extensibility and interoperability of the software through decoupling the data model from the user interface. Especially with assistance from the Python ecosystem, this software framework makes modern computer algorithms easier to be applied in bioimage analysis.

It implements the template matching function from the OpenCV library. The java interface of OpenCV was done through the javacv library. It is quite similar as the existing template matching plugin but runs much faster and users could choose among six matching methods: 

1.Squared difference

2.Normalized squared difference

3.Cross-correlation

4.Normalized cross-correlation

5.Correlation coefficient

6.Normalized correlation coefficient

The detailed algorithms could be found here.

The cvMatch_Template will search a specific object (image pattern) over an image of interest by the user-specified method. 

Neuroconductor is an open-source platform for rapid testing and dissemination of reproducible computational imaging software, specialized in brain medical imaging (MRI, fMRI, DTI, etc...) but that could be used on a wider range of images. The goals of the project are to:

• provide a centralized repository of R software dedicated to image analysis;
• disseminate quickly software updates;
• educate a large, diverse community of scientists using detailed tutorials and short courses;
• ensure quality via automatic and manual quality controls; and
• promote reproducibility of image data analysis.

Based on the programming language R, Neuroconductor starts with 68 inter-operable packages that cover multiple areas of imaging including visualization, data processing and storage, and statistical inference. Neuroconductor accepts new R package submissions, which are subject to a formal review and continuous automated testing.

CaPTk is a software platform for analysis of radiographic cancer images, currently focusing on brain, breast, and lung cancer. CaPTk integrates advanced, validated tools performing various aspects of medical image analysis, that have been developed in the context of active clinical research studies and collaborations toward addressing real clinical needs. With emphasis given in its use as a very lightweight and efficient viewer, and with no prerequisites for substantial computational background, CaPTk aims to facilitate the swift translation of advanced computational algorithms into routine clinical quantification, analysis, decision making, and reporting workflow. Its long-term goal is providing widely used technology that leverages the value of advanced imaging analytics in cancer prediction, diagnosis and prognosis, as well as in better understanding the biological mechanisms of cancer development.

NiftyNet is a TensorFlow-based open-source convolutional neural networks (CNNs) platform for research in medical image analysis and image-guided therapy. NiftyNet’s modular structure is designed for sharing networks and pre-trained models. Using this modular structure you can:

• Get started with established pre-trained networks using built-in tools;
• Adapt existing networks to your imaging data;
• Quickly build new solutions to your own image analysis problems.