Abstract
We examined the ability of radiologists to detect pulmonary nodules in computed radiographic (CR) chest images subjected to lossy image compression. Low-contrast 1-cm diameter targets simulating noncalcified pulmonary nodules were introduced into clinical images and presented to ten radiologists in a series of two-alternative forced-choice (2AFC) observer experiments. The percentages of correct observer responses obtained while viewing noncompressed images (1:1) were compared with those obtained for the same images compressed 7:1, 16:1, 44:1, and 127:1. The images were compressed using a standard full-frame discrete cosine transform (DCT) technique. The degree of compression was determined by quantizing Fourier components in various frequency channels and then Huffman encoding the result. The data show a measurable decline in performance for each compression ratio. Through signal-to-noise ratio (SNR) analysis, we found that the reduction in performance was due primarily to the compression algorithm that increased image noise in the frequency channels of the signals to be detected.
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