Due to the amount of medical image data being produced and transferred over networks, employing lossy compression has been accepted by worldwide regulatory bodies. As expected, increasing the degree of compression leads to decreasing image fidelity. The extent of allowable irreversible compression is dependent on the imaging modality and the nature of the image pathology as well as anatomy. Interpolation, which often causes image distortion, has been extensively used to rescale images during radiological diagnosis. This work attempts to assess the quality of medical images after the application of lossy compression followed by rescaling. This research proposes a full-reference objective measure of quality for medical images that considers their deterministic and statistical properties. Statistical features are acquired from the frequency domain of the signal and are combined with elements of the structural similarity index (SSIM). The aim is to construct a model that is specialized for medical images and that could serve as a predictor of quality.
If the inline PDF is not rendering correctly, you can download the PDF file here.
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.
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.
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.
Detyna, J., Jeleń, L., & Jeleń, M. (2011). Role of Image Processing in the Cancer Diagnosis. Bio-Algorithms and Med-Systems, 7(4), 5–9.
European Society of Radiology (ESR). (2011). Usability of irreversible image compression in radiological imaging. A position paper by the European Society of Radiology (ESR).
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.
Jeleń, Ł., Lipiński, A., Detyna, J. & Jeleń, M. (2011). Grading breast cancer malignancy with neural networks. EDITORIAL BOARD, 47.
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.
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.
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.
Lehmann, T. M., Gonner, C., & Spitzer, K. (1999). Survey: Interpolation methods in medical image processing. IEEE transactions on medical imaging, 18(11), 1049-1075.
Marmolin, H. (1986). Subjective MSE measures. IEEE transactions on systems, man, and cybernetics, 16(3), 486-489.
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.
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.
Naït-Ali, A., & Cavaro-Ménard, C. (Eds.). (2008). Compression of biomedical images and signals. ISTE.
Strintzis, M. G. (1998). A review of compression methods for medical images in PACS. International journal of medical informatics, 52(1-3), 159–165.
Thévenaz, P., Blu, T., & Unser, M. (2000). Image interpolation and resampling. Handbook of medical imaging, processing and analysis, 1(1), 393–420.
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.
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.
Wang, Z., & Li, Q. (2010). Information content weighting for perceptual image quality assessment. IEEE Transactions on image processing, 20(5), 1185–1198.