Abstract: Lossy compression drastically reduces the operating costs of digital medical imaging systems by allowing more efficient use of transmission and archival facilities in hospitals and doctors' offices. To be able to develop standards for the application of this technology, reliable tools are needed for measuring the quality of reconstructed images. Among the most common measures presently used, the normalized mean squared error (NMSE) does not provide any information concerning the type of impairment, and receiver operating characteristic (ROC) analyses are expensive and time-consuming. This paper evaluates the performance of three quantitative multi-dimensional measures for image quality. Mimicking the human visual system, they compute local features, and produce a graphical output. Eskicioglu charts, in particular, are shown to be an appropriate tool for characterizing compression losses in reconstructed medical images.
Index Terms:
medical image processing; image reconstruction; quality control; losses; data compression; image coding; standardisation; multi-dimensional quality measures; reconstructed medical images; lossy compression; operating costs; digital medical imaging systems; transmission facilities; archival facilities; hospitals; doctors' offices; standards development; reconstructed image quality; normalized mean squared error; receiver operating characteristic; human visual system; local features; graphical output; Eskicioglu charts; compression losses
Citation:
A.M. Eskicioglu, "Application of Multi-Dimensional Quality Measures to Reconstructed Medical Images," cbms, pp.0006, Eighth IEEE Symposium on Computer-Based Medical Systems (CBMS'95), 1995