Paper
27 March 1996 Nodule detection performance in compressed chest CT images
Uri Feldman, Philip F. Judy, Steven E. Seltzer, Ugur Topal, Christine Wester
Author Affiliations +
Abstract
An investigation was performed to evaluate objectively observer's ability to find lung nodules on compressed spiral computerized tomographic (CT) images of the chest. A set of 80 images from 13 patients served as backdrops. One simulated nodule of either 3.0, 3.4, 4.0, or 5.0 mm in diameter was inserted into each image. These 80 images were viewed on a computer screen in two formats: compressed with a wavelet transform coder at a compression rate of 40:1, and in the uncompressed 8 bit-per-pixel format, windowed down from the 12 bit-per- pixel originals. The images were presented one at a time in random order, as two conditions of 80 images each. Six observers searched for lung nodules on both the original and compressed formats. The tasks were to locate the nodule in each image, and, using a five category rating scale, to indicate the confidence that the indicated location contained a nodule. The results indicate that all observers detected a higher fraction of nodules in the original images than in the compressed images. Even though the compressed images were described by the observers as unacceptable for clinical use because they contained numerous artifacts, the percentage of 4 and 5 mm nodules found in the compressed images was high. Directions of further research include measurement of detection performance at lower compression rates, identification of compression artifacts that get confused with nodules, and analysis of the confidence ratings.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Uri Feldman, Philip F. Judy, Steven E. Seltzer, Ugur Topal, and Christine Wester "Nodule detection performance in compressed chest CT images", Proc. SPIE 2712, Medical Imaging 1996: Image Perception, (27 March 1996); https://doi.org/10.1117/12.236848
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KEYWORDS
Image compression

Computed tomography

Chest

Lung

Wavelets

Signal to noise ratio

Image quality

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