Subjective Versus Objective Assessment for Magnetic Resonance Images
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.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:71415", author = "Heshalini Rajagopal and Li Sze Chow and Raveendran Paramesran", title = "Subjective Versus Objective Assessment for Magnetic Resonance Images", 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).", volume = "9", number = "12", pages = "2426-6", }
