Multidimensional image processing and analysis in R

June 2, 2014 by Linda Vu
Multidimensional image processing and analysis in R
Talita Perciano

Many researchers believe that an esoteric, open-source programming language for statistical analysis—called R—could pave the way for open science. Today, thousands of international scientists are participating in the R development community—contributing new tools and libraries, including some that branch away from statistical analysis. And that number is rapidly growing.

One such contributor is Talita Perciano, a postdoctoral researcher in the Lawrence Berkeley National Laboratory's (Berkeley Lab's) Computational Research Division's Visualization Group. As a graduate student, Perciano contributed one of the first image-processing tools—called R Image Processing Analysis (RIPA)—to the community. Now with big science datasets in mind, she's updated the existing tool with improved features for complex data analysis.

"When I started working on RIPA in 2005 as an undergraduate student, the idea was to create an image processing package to analyze satellite images. My advisor and I developed the tools, which helped us to write several manuals and lectures on the topic. This material was the basis for our book Introduction to Image Processing using R," says Perciano. "At Berkeley Lab, we are dealing with bigger and more complex experimental and simulated scientific datasets. So I've been updating RIPA to run on massively parallel clusters at the National Energy Research Scientific Computing (NERSC) facility and perform much more advanced image processing tasks."

Perciano notes that the most recent RIPA release allows its users to do some essential image processing in parallel, like thresholding, changing contrast and brightness, and filtering. But the next version (to be released later this year) will be able to perform more complex image processing tasks in parallel. It will include new algorithms for pattern recognition and feature extraction, and it will be able to handle three-dimensional images. Perciano is also working on a new book about how to use the updated tools

Traditionally, privately owned tools like MATLAB (the mathematical computing software) and SAS (the statistical tool) have been necessities in research laboratories, similar to the way Microsoft Office is in office settings. But these tools can be expensive, and they have some limitations.

In fact, this is how Berkeley Lab Data Scientist Daniela Ushizima discovered RIPA. "I was working with a NERSC user on a project to visualize graphical patterns from massive datasets, such as Flickr images and TIME magazine covers for a cultural analytics project," she says. "The number of images was large and required parallel processing. Because of the scale of the problem, MATLAB was not an option, so we had to look for other analytics tools."

After searching the literature for alternatives, Ushizima found RIPA. "This tool was perfect because R is the lingua franca for and RIPA gave us many image processing capabilities inside R," says Ushizima. "Because R is open source, there is an extensive community of users and developers to support the creation of R-based algorithms and packages."

Today, R is used in a range of scientific disciplines from astronomy to genomics, and even in drug development. Because it is an open-source statistical framework, it allows users to quickly share techniques with other R users, as well as reproduce and reuse the techniques they have discovered. New codes and techniques are shared through groups like the Comprehensive R Archive Network (CRAN), which is where Perciano published her package.

"RIPA is one of the best /analysis packages in R," says Ushizima, who works with Perciano in image analysis and recognition at LBNL.

Explore further: OpenMSI: A science gateway to sort through bio-imaging's big datasets

More information: For more information about R:

More information about RIPA:

Related Stories

A new mathematics for experimental science

April 1, 2014

Mathematics is the ultimate scientific tool. For centuries it has been used to describe the forces of nature, from planetary motion to fluid dynamics. It helped unlock the secrets of DNA and unleashed the Digital Revolution. ...

Recommended for you

The ethics of robot love

November 25, 2015

There was to have been a conference in Malaysia last week called Love and Sex with Robots but it was cancelled. Malaysian police branded it "illegal" and "ridiculous". "There is nothing scientific about sex with robots," ...

No lens? No problem for FlatCam

November 23, 2015

How thin can a camera be? Very, say Rice University researchers who have developed patented prototypes of their technological breakthrough.

1 comment

Adjust slider to filter visible comments by rank

Display comments: newest first

not rated yet Jun 02, 2014
R's libraries are heavily tilted toward statistics but the base language is completely general purpose and quite interesting as such. As a language it can do anything Matlab can do and is more coherent. Library transliteration is not out of the question and is enhanced by the fact that both languages use 1 origin array indexing, a semantic property that is particularly nasty to transliterate if different.

Please sign in to add a comment. Registration is free, and takes less than a minute. Read more

Click here to reset your password.
Sign in to get notified via email when new comments are made.