Accès libre

The fidelity of compressed and interpolated medical images

À propos de cet article

Citez

Abdar, M., Książek, W., Acharya, U. R., Tan, R. S., Makarenkov, V., & Pławiak, P. (2019). A new machine learning technique for an accurate diagnosis of coronary artery disease. Computer methods and programs in biomedicine, 179, 104992.10.1016/j.cmpb.2019.104992Search in Google Scholar

Cheeseman, A. K., Kowalik-Urbaniak, I. A., & Vrscay, E. R. (2016, July). Objective Image Quality Measures of Degradation in Compressed Natural Images and their Comparison with Subjective Assessments. In International Conference on Image Analysis and Recognition (pp. 163–172). Cham: Springer.10.1007/978-3-319-41501-7_19Search in Google Scholar

Cosman, P. C., Gray, R. M., & Olshen, R. A. (1994). Evaluating quality of compressed medical images: SNR, subjective rating, and diagnostic accuracy. Proceedings of the IEEE, 82(6), 919–932.10.1109/5.286196Search in Google Scholar

Detyna, J., Jeleń, L., & Jeleń, M. (2011). Role of Image Processing in the Cancer Diagnosis. Bio-Algorithms and Med-Systems, 7(4), 5–9.Search in Google Scholar

European Society of Radiology (ESR). (2011). Usability of irreversible image compression in radiological imaging. A position paper by the European Society of Radiology (ESR).Search in Google Scholar

George, A., & Livingston, S. J. (2013). A survey on full reference image quality assessment algorithms. International Journal of Research in Engineering and Technology, 2(12), 303–307.10.15623/ijret.2013.0212052Search in Google Scholar

Jeleń, Ł., Lipiński, A., Detyna, J. & Jeleń, M. (2011). Grading breast cancer malignancy with neural networks. EDITORIAL BOARD, 47.Search in Google Scholar

Kowalik-Urbaniak I.A. (2014) The quest for ‘diagnostically lossless’ medical image compression using objective image quality measures. PhD thesis, Waterloo, Canada: University of Waterloo.10.1117/12.2043196Search in Google Scholar

Kowalik-Urbaniak, I., Brunet, D., Wang, J., Koff, D., Smolarski-Koff, N., Vrscay, E. R., & Wang, Z. (2014, March). The quest for’diagnostically lossless’ medical image compression: a comparative study of objective quality metrics for compressed medical images. In Medical Imaging 2014: Image Perception, Observer Performance, and Technology Assessment (Vol. 9037, p. 903717). International Society for Optics and Photonics.10.1117/12.2043196Search in Google Scholar

Kowalik-Urbaniak, I. A., Castelli, J., Hemmati, N., Koff, D., Smolarski-Koff, N., Vrscay, E. R., & Wang, Z. (2015, July). Modelling of subjective radiological assessments with objective image quality measures of brain and body CT images. In International Conference Image Analysis and Recognition (pp. 3–13). Cham: Springer.10.1007/978-3-319-20801-5_1Search in Google Scholar

Lehmann, T. M., Gonner, C., & Spitzer, K. (1999). Survey: Interpolation methods in medical image processing. IEEE transactions on medical imaging, 18(11), 1049-1075.10.1109/42.816070Search in Google Scholar

Marmolin, H. (1986). Subjective MSE measures. IEEE transactions on systems, man, and cybernetics, 16(3), 486-489.10.1109/TSMC.1986.4308985Search in Google Scholar

Meijering, E. H. (2000, September). Spline interpolation in medical imaging: comparison with other convolution-based approaches. In 2000 10th European Signal Processing Conference (pp. 1–8). IEEE.Search in Google Scholar

Meijering, E. H., Niessen, W. J., & Viergever, M. A. (2001). Quantitative evaluation of convolution-based methods for medical image interpolation. Medical image analysis, 5(2), 111–126.10.1016/S1361-8415(00)00040-2Search in Google Scholar

Naït-Ali, A., & Cavaro-Ménard, C. (Eds.). (2008). Compression of biomedical images and signals. ISTE.10.1002/9780470611159Search in Google Scholar

Strintzis, M. G. (1998). A review of compression methods for medical images in PACS. International journal of medical informatics, 52(1-3), 159–165.10.1016/S1386-5056(98)00135-XSearch in Google Scholar

Thévenaz, P., Blu, T., & Unser, M. (2000). Image interpolation and resampling. Handbook of medical imaging, processing and analysis, 1(1), 393–420.10.1016/B978-012077790-7/50030-8Search in Google Scholar

Wang, Z., & Bovik, A. C. (2009). Mean squared error: Love it or leave it? A new look at signal fidelity measures. IEEE signal processing magazine, 26(1), 98–117.10.1109/MSP.2008.930649Search in Google Scholar

Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13(4), 600–612.10.1109/TIP.2003.819861Search in Google Scholar

Wang, Z., & Li, Q. (2010). Information content weighting for perceptual image quality assessment. IEEE Transactions on image processing, 20(5), 1185–1198.10.1109/TIP.2010.209243521078577Search in Google Scholar