Seeing the Future with Imaging Science Interdisciplinary Research Team Summaries (2011) / Chapter Skim
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IDR Team Summary 2: Identify the mathematical and computational tools that are needed to bring recent insights from theoretical image science and rigorous methods of task-based assessment of image quality into routine use in all areas of imaging.
IDR Team Summary 2: Identify the mathematical and computational tools that are needed to bring recent insights from theoretical image science and rigorous methods of task-based assessment of image quality into routine use in all areas of imaging.
Pages 21-34
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From page 21... ...
Generically, the tasks can be classification of the objects being imaged, estimation of object parameters, or a combination of both. The means by which the task is performed is called the observer, a term that can refer to a human, some ad hoc computer algorithm, the ideal Bayesian observer who gets the best possible task performance, and various linear approximations to the ideal observer.
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From page 22... ...
The object randomness leads to randomness in the images; therefore, it is important to understand how to transform the object statistics through the imaging system; again, nonlinear systems pose difficulties. In addition, there is always noise arising from the measurement process, for example Gaussian noise in the electronics or Poisson noise in photon-counting detectors.
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From page 23... ...
What gold standards would be used for assessing task performance with real data? Would there be interest in methods for assessment with real data but with no reliable gold standard?
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From page 24... ...
Team 2A was comprised of researchers armed with a broad arsenal of imaging knowledge, including expertise in consumer imaging, which is imaging that deals with products for consumers, optical imaging, and imaging in engineering, astronomy, and computer science. During two days at the conference, the IDR team debated long and hard about the best way to identify the tools that are needed to bring insights from theoretical image science and rigorous methods of task-based assessment of image quality into routine use in all areas of imaging.
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From page 25... ...
In task-based assessment of image quality, however, the FOM should ideally represent the ability of the image to help the observer complete the "task," whether the task is detecting a tumor on an MRI, measuring the power spectrum of microwave background in astronomy research, or classifying a forest as deciduous versus coniferous from a remote sensing image. For an imaging system, the FOM represents performance ability, that is, how helpful is the produced image.
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From page 26... ...
This often involves reconstruction algorithms, general restoration (including noise reduction) , and specific processing geared toward the specific observer.
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From page 27... ...
IDR TEAM MEMBERS -- GROUP B • Ali Bilgin, University of Arizona • Mark A. Griswold, Case Western Reserve University • Hamid Jafarkhani, University of California, Irvine • Thrasyvoulos N. Pappas, Northwestern University • P. Jonathon Phillips, National Institute of Standards and Technology • Joshua W. Shaevitz, Princeton University • Remy Tumbar, Cornell University • Tom Vogt, University of South Carolina • Emily White, Texas A&M IDR TEAM SUMMARY -- GROUP B Emily White, NAKFI Science Writing Scholar, Texas A&M Key Questions (modified by IDR team from original assignment) How can task-based assessment be achieved?
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From page 28... ...
Databases of such standardized input ensembles, and of the gold standard output ensembles, would exist for all conceivable tasks. For non-simulated tests, easily transportable calibration samples would be validated at multiple locations and then used to evaluate new systems and any modifications to existing systems.
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From page 29... ...
Although current models might be helpful in the development of new systems, the statistical descriptions of system components are not currently complete enough for model simulations to be fully predictive of system output, necessitating assessment approaches that use real inputs and data. However, standardized input ensembles also do not exist, and we currently lack an understanding of how many and what variety of images would be needed to best inform assessment.
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From page 30... ...
For example, the observer is likely a highly influential part of the imaging chain with respect to task performance, and uniform, reproducible performance by human observers may be unlikely. In many current imaging applications, a human is still the ideal observer given current technology.
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From page 31... ...
For example, the raw data output of a modern 3-D ultrasound system would be virtually impossible for a person to visualize, yet data modeling permits real-time 3-D computer reconstructions that are easily interpreted by human observers. However, not all image outputs are ideal; some imaging systems have shortcomings in data modeling, thus limiting the capability of the image to describe what we want to know about the object.
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From page 32... ...
These infrastructure considerations also should account for the observer (as discussed above) : There is a cost to human learning, and retraining observers, particularly human observers, to perform tasks using an improved system output could be prohibitively costly.
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From page 33... ...
Scientists can also identify or develop gold standards for imaging and create databases, also for widespread use. These input and output ensembles could be used to assess existing imaging systems via a "round robin" approach (imaging one input ensemble using many systems)
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From page 34... ...
to achieve adequate modeling. It is also worthwhile to consider whether some simple models might have the capacity to adequately inform performance assessment.
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