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IDR Team Summary 3: Develop and validate new methods for detecting and classifying meaningful changes between two images taken at different times or within temporal sequences of images.
Pages 35-52

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From page 35... ... Technical factors can also change: the magnification might be slightly different between two aerial images, or a different X-ray tube voltage or amount of contrast agent might have been used for two different CT images. These kinds of change are easily detected simply by subtracting two images, but the resulting difference image could still convey no meaningful information about the important changes for which the image are being compared. Read the entire page →
From page 36... ... A different approach is to recognize key components of the evolving images and their spatiotemporal relation to one another. This semantic approach is similar in spirit to what the human visual and cognitive system does in analyzing scenes containing well-delineated, temporally varying object components, but computer implementations can take into account the noise and resolution characteristics of the images. Read the entire page →
From page 37... ... IDR TEAM MEMBERS -- GROUP A • Mark Bathe, Massachusetts Institute of Technology • Felice C. Frankel, Harvard Medical School • Ana Kasirer-Friede, University of California, San Diego • K. J. Ray Liu, University of Maryland • Joseph A. O'Sullivan, Washington University • Robert B. Pless, Washington University • Jerilyn A. Timlin, Sandia National Laboratories • Derek K. Toomre, Yale University • Paul S. Weiss, University of California, Los Angeles • Jessika Walsten, University of Southern California IDR TEAM SUMMARY -- GROUP A Jessika Walsten, NAKFI Science Writing Scholar, University of Southern California IDR team 3A wrestled with the problem of defining meaningful changes among images. These changes can be between two images or in a series of images over a period of time. Read the entire page →
From page 38... ... Because there are so many variables at play when images are analyzed (e.g., instrumentation, light, vibration, resolution, etc.) , the IDR team thought it necessary to somewhat narrow the scope of its original challenge, which was to: Develop and validate new methods for detecting and classifying meaningful changes between two images taken at different times or within temporal sequences of images. Read the entire page →
From page 39... ... The word trend is similarly ambiguous, meaning different things to different people. In general, however, the team defined a trend as meaningful changes over time. Read the entire page →
From page 40... ... Factor analysis looks at the raw numbers and finds trends in the numbers. For example, if a video of cell vesicle fusion events is analyzed via a statistical program, like MATLAB, the program will average all of the images in the video into a composite image. Read the entire page →
From page 41... ... This could also mean using tools or theories in disparate fields to help analyze the data or solve problems collaboratively. One example of this is pattern theory, a mathematical theory that tries to explain changes in images using combinations of a few fundamental operations. Read the entire page →
From page 42... ... If researchers develop these areas, they will better be able to find meaningful trends in images and find ways to improve their data analysis from those images. IDR TEAM MEMBERS -- GROUP B • Daniel F. Keefe, University of Minnesota • Lincoln J. Lauhon, Northwestern University • Mohammad H. Mahoor, University of Denver • Giovanni Marchisio, DigitalGlobe • Emmanuel G. Reynaud, University College Dublin • James E. Rhoads, Arizona State University • Bernice E. Rogowitz, University of Texas, Austin • Demetri Terzopoulos, University of California, Los Angeles • Rene Vidal, Johns Hopkins University • Emily Ruppel, Massachusetts Institute of Technology IDR TEAM SUMMARY -- GROUP B Emily Ruppel, NAKFI Science Writing Scholar, Massachusetts Institute of Technology Current Imaging Methods: Not Always a Clear Picture Imagine walking into an empty room, turning on the light, and taking a picture of a chair. Read the entire page →
From page 43... ... If anything about the calibration of the MRI device changes, if there is some methodological change between two brain imaging sessions, the resulting data could suggest changes that have nothing to do with whether the tumor is shrinking or growing or spreading. Meaningful detection of change is not just a problem in neurology -- scientists in many fields are eager to establish better methods for capturing and determining significant change based on highly reliable imaging. Read the entire page →
From page 44... ... With no "fix-all" that, when reduced to computation, could help determine meaningful change in every situation, IDR team 3B focused on developing a model that scientists in their respective fields can use to solve their own imaging problems, using creative algorithms where necessary and/or possible. The Man Machine While the vertical flow of this model focuses on the relationship between humans and their equipment, the horizontal arrows point to the heart of the matter: the relationship between humans and how they can use representations to help a computer "see" their data. Read the entire page →
From page 45... ... If humans become active participants in not only evaluating the effectiveness of their respective systems, but also reimagining the computers that are often merely their tools, they open up possibilities for creative computerization and previously unseen solutions. The best example for how to value human creativity in this particular kind of problem solving comes from a rather surprising source: the unfolding of our own understanding. Read the entire page →
From page 46... ... , IDR team 3B thinks that its introduction to and integration of all methods of imaging will help set the foundation to "begin building a new generation in human/computer interaction, which will enable us to envision a new era of understanding in the representation and analysis of complex images." IDR TEAM MEMBERS -- GROUP C • Sima Bagheri, New Jersey Institute of Technology • David A. Fike, Washington University • Douglas P. Finkbeiner, Harvard University • Eric Gilleland, National Center for Atmospheric Research • David M. Hondula, The University of Virginia • Jonathan J. Makela, University of Illinois at Urbana-Champaign • Mahta Moghaddam, The University of Michigan • Naoki Saito, University of California, Davis • Curtis Woodcock, Boston University • Olga Khazan, University of Southern California IDR TEAM SUMMARY -- GROUP C Olga Khazan, NAKFI Science Writing Scholar, University of Southern California Scientists who rely on images to provide data are faced with an unusual challenge: Although taking two images is easy, finding the scientific difference between the two images remains a much more complicated task. Scientists trying to find the differences between two images often find Read the entire page →
From page 47... ... Because of this segregation of image detection methods between physicists and climatologists, for example, researchers frequently find them selves "stuck" doing change detection as it has always been done in their field, which can stymie the progress of change detection methods overall. IDR team 3C, at the National Academies Keck Futures Initiative Conference on Imaging Science, was tasked with "developing and validating new methods for detecting and classifying meaningful changes between images." Although standard methods for differentiating images already exist for everyone from astronomers to zoologists, scientists from different areas suffer from a lack of communication about these methods. Read the entire page →
From page 48... ... Therefore, in order to make sensible decisions based on changes in images, scientists need to know what they're measuring and why. Methods for Good Change Detection It's impossible to model an entire forest or an ocean, so imaging specialists choose a set of parameters, or dimensions, that they can use to characterize an image. Read the entire page →
From page 49... ... Computational issues, poorly measured interference, imperfect algorithms, and the nonlinear nature of certain problems can all make it hard to generate an accurate image from the data collected. There's no way to know that the model results are close to reality. Read the entire page →
From page 50... ... Many scientists who actually perform image analysis were never academically trained in the practice, and instead learned on the job from others in their own profession. In order to overcome these myriad obstacles, the IDR team proposes the creation of a common framework to detect changes in images across disciplines. Read the entire page →
From page 51... ... By seeing the various approaches to the challenge, the original creators of the data set and images could see if there was a new or better approach to the change detection than the one they had been using With these two solutions -- the data challenge and online repository -- image analysts would be better able to apply existing solutions to their current problem. That way, scientists from multiple fields would be provided with not only their own tools for detecting changes, but also those of their colleagues from other disciplines. Read the entire page →

From page 35...

... Technical factors can also change: the magnification might be slightly different between two aerial images, or a different X-ray tube voltage or amount of contrast agent might have been used for two different CT images. These kinds of change are easily detected simply by subtracting two images, but the resulting difference image could still convey no meaningful information about the important changes for which the image are being compared.

IDR Team Summary 3: Develop and validate new methods for detecting and classifying meaningful changes between two images taken at different times or within temporal sequences of images. Pages 35-52

IDR Team Summary 3: Develop and validate new methods for detecting and classifying meaningful changes between two images taken at different times or within temporal sequences of images.
Pages 35-52