RATIONALE AND OBJECTIVES: We investigated non-receiver operating characteristic (non-ROC) methods for the selection of processing algorithms for digital image compression. METHODS: We performed a multipoint, rank-order study with 20 posteroanterior chest images, each processed using four different algorithms. Seven radiologists reviewed these alongside the digitized noncompressed image. Observers were forced to rank order the similarity and/or difference of the processed images to the nonprocessed image in each case. RESULTS: A two-way analysis of variance of the rankings was statistically significant (p = .025), indicating that one processing scheme yielded images that were clearly perceived as the most similar to the nonprocessed images. The selected processing scheme was not the one that yielded the lowest quantitative difference from the nonprocessed images as measured by root mean square error. CONCLUSION: Non-ROC study designs that are highly sensitive to small differences among similar images can be used to select processing algorithms.
RATIONALE AND OBJECTIVES: We investigated non-receiver operating characteristic (non-ROC) methods for the selection of processing algorithms for digital image compression. METHODS: We performed a multipoint, rank-order study with 20 posteroanterior chest images, each processed using four different algorithms. Seven radiologists reviewed these alongside the digitized noncompressed image. Observers were forced to rank order the similarity and/or difference of the processed images to the nonprocessed image in each case. RESULTS: A two-way analysis of variance of the rankings was statistically significant (p = .025), indicating that one processing scheme yielded images that were clearly perceived as the most similar to the nonprocessed images. The selected processing scheme was not the one that yielded the lowest quantitative difference from the nonprocessed images as measured by root mean square error. CONCLUSION: Non-ROC study designs that are highly sensitive to small differences among similar images can be used to select processing algorithms.