The overall goal is to optimize the design of medical imaging systems and reconstruction algorithms for the purposes of tumor detection in humans and animals. For imaging systems this is done by devising efficient methods to calculate the performance of ideal observers that use the noisy data from the system on realistic tumor detections tasks. These methods make use of symmetries of the imaging system, consistency conditions on the data, and constraints on the objects to simplify the computations. Noise models are chosen to reflect the measurement noise in the system, the background variation in the patient population, and the normal variation in tumor characteristics. Performance of ideal observers is determined by the area under the receiver operating characteristic curve. For a given detection task, this performance is a function of the parameters in the system design. By varying these parameters the optimal design for tumor detection is found by maximizing this figure of merit. When the ideal observer performance is too difficult to compute, the ideal linear observer is substituted. For reconstruction algorithms, the performance of linear mathematical observers using the reconstructed images for tumor detection tasks is computed. These model observers are chosen to match the performance of human observers on similar tasks. To compute the performance of these observers, the first-order and second-order statistics of the reconstructed images are calculated or approximated. These statistical moments are then used to compute the signal-to-noise ratio for the model observer on the given tumor detection task. Of particular interest is the role of redundant data and null functions of the imaging system in the deterministic and statistical properties of the reconstruction algorithms.
| Clarkson, Edward; Desai, Krishna; Foley, James (2009) ResultMaps: visualization for search interfaces. IEEE Trans Vis Comput Graph 15:1057-64 |
| Clarkson, Eric; Kupinski, Matthew A; Barrett, Harrison H (2006) A probabilistic model for the MRMC method, part 1: theoretical development. Acad Radiol 13:1410-21 |
| Kupinski, Matthew A; Clarkson, Eric; Barrett, Harrison H (2006) A probabilistic model for the MRMC method, part 2: validation and applications. Acad Radiol 13:1422-30 |
| Lee, Kye-Sung; Akcay, A Ceyhun; Delemos, Tony et al. (2005) Dispersion control with a Fourier-domain optical delay line in a fiber-optic imaging interferometer. Appl Opt 44:4009-22 |
| Park, Subok; Clarkson, Eric; Kupinski, Matthew A et al. (2005) Efficiency of the human observer detecting random signals in random backgrounds. J Opt Soc Am A Opt Image Sci Vis 22:3-16 |
| Rolland, Jannick; O'Daniel, Jason; Akcay, Ceyhun et al. (2005) Task-based optimization and performance assessment in optical coherence imaging. J Opt Soc Am A Opt Image Sci Vis 22:1132-42 |
| Akcay, A Ceyhun; Clarkson, Eric; Rolland, Jannick P (2005) Effect of source spectral shape on task-based assessment of detection and resolution in optical coherence tomography. Appl Opt 44:7573-80 |
| Kupinski, Matthew A; Clarkson, Eric; Hoppin, John W et al. (2003) Experimental determination of object statistics from noisy images. J Opt Soc Am A Opt Image Sci Vis 20:421-9 |
| Hoppin, John W; Kupinski, Matthew A; Kastis, George A et al. (2002) Objective comparison of quantitative imaging modalities without the use of a gold standard. IEEE Trans Med Imaging 21:441-9 |
| Kupinski, Matthew A; Hoppin, John W; Clarkson, Eric et al. (2002) Estimation in medical imaging without a gold standard. Acad Radiol 9:290-7 |
Showing the most recent 10 out of 11 publications
