Subjective Versus Objective Assessment for Magnetic Resonance Images

Authors: Heshalini Rajagopal, Li Sze Chow, Raveendran Paramesran

Abstract:

Magnetic Resonance Imaging (MRI) is one of the most important medical imaging modality. Subjective assessment of the image quality is regarded as the gold standard to evaluate MR images. In this study, a database of 210 MR images which contains ten reference images and 200 distorted images is presented. The reference images were distorted with four types of distortions: Rician Noise, Gaussian White Noise, Gaussian Blur and DCT compression. The 210 images were assessed by ten subjects. The subjective scores were presented in Difference Mean Opinion Score (DMOS). The DMOS values were compared with four FR-IQA metrics. We have used Pearson Linear Coefficient (PLCC) and Spearman Rank Order Correlation Coefficient (SROCC) to validate the DMOS values. The high correlation values of PLCC and SROCC shows that the DMOS values are close to the objective FR-IQA metrics.

Keywords: Medical Resonance (MR) images, Difference Mean Opinion Score (DMOS), Full Reference Image Quality Assessment (FR-IQA).

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1110075

References:

[1] B. Mortamet, M. a. Bernstein, C. R. Jack, J. L. Gunter, C. Ward, P. J. Britson, R. Meuli, J. P. Thiran, and G. Krueger, “Automatic quality assessment in structural brain magnetic resonance imaging,” Magn. Reson. Med., vol. 62, pp. 365–372, 2009.
[2] R. Kumar and M. Rattan, “Analysis Of Various Quality Metrics for Medical Image Processing,” Int. J. Adv. Res. Comput. Sci. Softw. Eng., vol. 2, no. 11, pp. 137–144, 2012.
[3] K. Bindu, A. Ganpati, and A. K. Sharma, “A Comparative Study of Image Compression Algorithms,” Int. J. Res. Comput. Sci., vol. 2, no. 5, pp. 37–42, 2012.
[4] H. R. Sheikh, M. F. Sabir, and A. C. Bovik, “A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms,” Image Process. IEEE Trans., vol. 15, no. 11, pp. 3441–3452, 2006.
[5] N. Ponomarenko, V. Lukin, K. Egiazarian, J. Astola, M. Carli, and F. Battisti, “Color image database for evaluation of image quality metrics,” in Multimedia Signal Processing, 2008 IEEE 10th Workshop on, 2008, pp. 403–408.
[6] L. S. Chow and H. Rajagopal, “Comparison of Difference Mean Opinion Score (DMOS) of Magnetic Resonance Images with Full- Reference Image Quality Assessment (FR-IQA),” in Image Processing, Image Analysis and Real-Time Imaging (IPARTI) Symposium, 2015.
[7] “MR images from Osirix DICOM Viewer.” (Online). Available: http://www.osirix-viewer.com/datasets/. (Accessed: 20-Jan-2015).
[8] H. Gudbjartsson and S. Patz, “The Rician distribution of noisy MRI data,” Magn. Reson. Med., vol. 34, no. 6, pp. 910–914, 1995.
[9] A. Debnath, H. M. Rai, C. Yadav, and A. Bhatia, “Deblurring and Denoising of Magnetic Resonance Images using Blind Deconvolution Method,” Int. J. Comput. Appl., vol. 81, no. 10, pp. 7–12, 2013.
[10] R. L. de Queiroz, “DCT approximation for low bit rate coding using a conditional transform,” in Image Processing. 2002. Proceedings. 2002 International Conference on, 2002, vol. 1, pp. 237–240.
[11] R. C. Gonzalez and R. E. Woods, Digital Image Processing (3rd Edition). Prentice-Hall, Inc., 2006.
[12] Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: From error visibility to structural similarity,” Image Process. IEEE Trans., vol. 13, no. 4, pp. 600–612, 2004.
[13] N. Damera-Venkata, T. D. Kite, W. S. Geisler, B. L. Evans, and a C. Bovik, “Image quality assessment based on a degradation model.,” IEEE Trans. Image Process., vol. 9, no. 4, pp. 636–650, 2000.
[14] H. R. Sheikh and A. C. Bovik, “Image information and visual quality.,” IEEE Trans. Image Process., vol. 15, no. 2, pp. 430–444, 2006.
[15] X.-K. Song, Correlated data analysis: modeling, analytics, and applications. Springer Science & Business Media, 2007.
[16] T. D. Gautheir, “Detecting Trends Using Spearman’s Rank Correlation Coefficient,” Environmental Forensics, vol. 2, no. 4. Taylor & Francis, pp. 359–362, 2001.
[17] R. Taylor, “Interpretation of the Correlation Coefficient: A Basic Review,” J. Diagnostic Med. Sonogr., vol. 6, no. 1, pp. 35–39, 1990.
[18] A. Vibhakar, M. Tiwari, and J. Singh, “Performance Analysis for MRI Denoising using Intensity Averaging Gaussian Blur Concept and its Comparison with Wavelet Transform Method,” Int. J. Comput. Appl., vol. 58, no. 15, pp. 21–26, 2012.
[19] M. Ertas, I. Yildirim, M. Kamasak, and A. Akan, “An iterative tomosynthesis reconstruction using total variation combined with nonlocal means filtering.,” Biomed. Eng. Online, vol. 13, no. 1, p. 65, 2014.
[20] A. B. Watson, “Image Compression Using the Discrete Cosine Transform,” Math. J., vol. 4, no. 1, pp. 81–88, 1994.
[21] A. Mittal, A. K. Moorthy, and A. C. Bovik, “No-Reference Image Quality Assessment in the Spatial Domain,” Image Process. IEEE Trans., vol. 21, no. 12, pp. 4695–4708, 2012.