Automated

This method was originally designed to track objects (not necessarily spots) already identified in 2D 
frames and has been applied previously to particle tracking and analysis in high-speed atomic force microscopy image series.

The phase contrast microscopy segmentation toolbox (PHANTAST) is a collection of open-source algorithms and tools for the processing of phase contrast microscopy (PCM) images. It was developed at University College London's department of Biochemical Engineering and CoMPLEX.

Align_slices in stack utilized the template matching function cvMatch_Template to do slice registration(alignment) based on a selected landmark.
This function will try to find the landmark or the most similar image pattern in every slice and translate each slice so that the landmark pattern will be the same position throughout the whole stack. It could be used to fix the drift of a time-lapse image stacks.

Source code: link

Input data: image stack
output data: image stack

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.