Ultrasound Image Intensity Nonuniformity Correction by Combining Intensity and Spatial Information

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Abstract:

Medical ultrasonic B-scans often suffer from intensity inhomogeneities that originates from the nonuniform attenuation properties of the sonic beam within the body. In order to correct signal attenuation in the tissue, time gain compensation (TGC) is routinely used in medical ultrasound scanners. However, TGC assumes a uniform attenuation coefficient for all body tissues. Since this assumption is evidently inaccurate, over-amplification or under-amplification sometimes appear. This is a major problem for intensity-based, automatic segmentation of video-intensity images because conventional threshold-based or intensity-statistic-based approaches do not work well in the presence of such image distortions. The main contribution of this paper is that additional spatial image features are incorporated to improve inhomogeneity correction and to make it more dynamic besides most commonly used intensity features, so that local intensity variations can be corrected more efficiently. The degraded image is corrected by the inverse of the image degradation model. The image degradation process is described by a linear model, consisting of a multiplicative and an additive component which are modeled by a combination of smoothly varying basis functions. Spatial information about intensity nonuniformity is obtained using cubic spline smoothing and entropy minimizing. Gray-level histogram information of the image corrupted by intensity inhomogeneity is exploited from a signal processing perspective. We explain how this model can be related to the ultrasonic physics of image formation to justify our approach. Experiments are presented on synthetic images and real US data to evaluate quantitatively the accuracy of the method.

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552-557

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August 2013

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] C. Tuncer, Aysal, E. Kenneth and Barner, Rayleigh-Maximum-Likelihood Filtering for Speckle Reduction of Ultrasound Images, IEEE Trans. Med. Imaging, vol. 26, no. 5, (2007).

DOI: 10.1109/tmi.2007.895484

Google Scholar

[2] H. C. Huang, J. Y. Chen, S. D. Wang and C. M. Chen, Adaptive ultrasonic speckle reduction based on the slope-facet model, Ultrasound in Med. & Biol., Vol. 29, no. 8, pp.1161-1175, (2003).

DOI: 10.1016/s0301-5629(03)00927-x

Google Scholar

[3] M. Karaman, K. Alper and G. Bozdagi, An adaptive speckle suppression filter for medical ultrasound imaging, , IEEE Trans. Med. Imaging, vol. 14, pp.283-292, (1995).

DOI: 10.1109/42.387710

Google Scholar

[4] Zhong Tao, D. Hemant and Tagare, Evaluation of four probability distribution models for speckle in clinical cardiac ultrasound images, IEEE Trans. Med. Imaging, vol. 25, no. 11, pp.1483-1491, (2006).

DOI: 10.1109/tmi.2006.881376

Google Scholar

[5] A. Pizurica, W. Philips, I. Lemahieu, and M. Acheroy, A versatile wavelet domain noise filtration technique for medical imaging, IEEE Trans. on Medical Imaging, vol. 22, p.323– 331, (2003).

DOI: 10.1109/tmi.2003.809588

Google Scholar

[6] Y. Chen, R. Yin, Flynn and P. Broschat, Aggressive region growing for speckle reduction in ultrasound images, Pattern Recognition Letters, vol. 24 , no. 4-5, pp.677-691, (2003).

DOI: 10.1016/s0167-8655(02)00174-5

Google Scholar

[7] H. Spitzer and Y. Zimmer, Improvement of illumination artifacts in medical ultrasound images using a biologically based algorithm for compression of wide dynamic range, Proceedings of the 2004 11th IEEE International Conference on Electronics, Circuits and Systems, pp.435-487, (2004).

DOI: 10.1109/icecs.2004.1399711

Google Scholar

[8] J. Roth, A. Mandlik, J. Sandhu, L. Hertert, Approaches for non-uniformity correction and dynamic range extension for acoustography, SPIE Int. Soc. Opt. Eng., vol. 5770 , pp.124-134, (2005).

DOI: 10.1117/12.601034

Google Scholar

[9] Guofang Xiao, Michael Brady, etc, Segmentation of Ultrasound B-Mode Images With Intensity Inhomogeneity Correction, IEEE Trans. Med. Imag, vol. 21, no. 1, pp.48-57, (2002).

DOI: 10.1109/42.981233

Google Scholar

[10] E. A. Ashton and K. J. Parker, Multiple resolution Bayesian segmentation of ultrasound images, Ultrasound Imag., vol. 17, p.291–304, (1995).

DOI: 10.1006/uimg.1995.1014

Google Scholar

[11] D. Boukerroui, O. Basset, A. Baskurt, and G. Gimenez, A multiparametric and multiresolution segmentation algorithm of 3-D ultrasound data, IEEE Trans. Ultrason., Ferroelect. Freq. Contr., vol. 48, no. 1, p.64–77, (2001).

DOI: 10.1109/58.895909

Google Scholar

[12] Guofang Xiao, Michael Brady, J. Alison Noble and Yongyue Zhang, Contrast enhancement and segmentation of ultrasound images: a statistical method, Proc. SPIE Med. Imaging Image Processing , p.1116–1125, (2000).

DOI: 10.1117/12.387616

Google Scholar

[13] J. Luo, Y. Zhu, P. Clarysse, and I. Magnin, Correction of bias field in MR images using singularity function analysis, IEEE Trans. Med. Imag., vol. 24, no. 8, p.1067–1085, (2005).

DOI: 10.1109/tmi.2005.852066

Google Scholar

[14] C. R. Meyer, P. H. Bland, and J. Pipe, Retrospective correction of intensity inhomogeneities in MRI, IEEE Trans. Med. Imag, vol. 14, no. 1, p.36–41, (1995).

DOI: 10.1109/42.370400

Google Scholar

[15] J. Milles, Y. Zhu, N. Chen, L. Panych, G. Gimenez and C. Guttmann, MRI intensity nonuniformity correction using simultaneously spatial and gray-level histogram information, SPIE Medical Imaging, p.734–742, (2004).

DOI: 10.1117/12.532325

Google Scholar

[16] O. Salvado, C. Hillenbrand, S. Zhang, J. S. Suri and D. L. Wilson, MR Signal inhomogeneity correction for Visual and computerized atherosclerosis lesion assessment, proceeding of 2004 IEEE International Symposium on Biomedical Imaging, vol. 2, pp.1143-1146, (2004).

DOI: 10.1109/isbi.2004.1398745

Google Scholar