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Proceedings Paper

Investigation of JPEG 2000 encoder options on model observer performance in signal known exactly but variable tasks (SKEV)
Author(s): Yani Zhang; Binh Pham; Miguel P. Eckstein
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Paper Abstract

The new still image compression standard JPEG 2000 provides a set of features such as multiple resolution representation, tiling, region of interest (ROI) coding, and easy compression rate control. Previous evaluations of these encoder options have been with respect to non-task based image quality metrics (PSNR) and for non-medical images. In this paper, we investigated the effect of different JPEG 2000 encoder options on task-based model and human observer performance. Test images consisted of x-ray coronary angiogram backgrounds with simulated filling defects (signals)of 184 different size/shapes inserted in one of four simulated arteries. The task was to select the simulated artery containing the signal (four alternative forced choice; 4 AFC). The signal on each trial varied in shape and size but was known to the observer (signal known exactly but variable, SKEV). We obtained performance for the non-prewhitening matched filter with an eye filter (NPWE) model for the SKEV task through direct template implementation (Eckstein et al., 2000) on the test images. For comparison, a follow-up human psychophysical study with two observers was conducted. Our results showed that the dependence of task performance on the JPEG 2000 encoder options was similar for both the NPWE model and the human observers.

Paper Details

Date Published: 22 May 2003
PDF: 12 pages
Proc. SPIE 5034, Medical Imaging 2003: Image Perception, Observer Performance, and Technology Assessment, (22 May 2003); doi: 10.1117/12.480078
Show Author Affiliations
Yani Zhang, Univ. of California/Santa Barbara (United States)
Binh Pham, Univ. of California/Santa Barbara (United States)
Miguel P. Eckstein, Univ. of California/Santa Barbara (United States)


Published in SPIE Proceedings Vol. 5034:
Medical Imaging 2003: Image Perception, Observer Performance, and Technology Assessment
Dev P. Chakraborty; Elizabeth A. Krupinski, Editor(s)

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