Abstract
Medical ultrasound imaging is an important tool in diagnosing and monitoring synovitis, which is an inflammation of the synovial membrane that surrounds a joint. Ultrasound images are examined by medical experts to assess the presence and progression of synovitis. Automating image analysis reduces the costs and increases the availability of the ultrasound diagnosis of synovitis and diminishes or eliminates subjective discrepancies. This article describes research that is concerned with the problem of the automatic estimation of the state of the activity of finger joint inflammation using the information that is present in ultrasonography imaging.
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References
Ostergaard, M., Szkudlarek, M.: Ultrasonography: a valid method for assessing rheumatoid arthritis? Arthritis Rheum. 52(3), 681–686 (2005)
Kamishima, T., Tanimura, K., Henmi, M., Narita, A., Sakamoto, F., Terae, S., Shirato, H.: Power Doppler ultrasound of rheumatoid synovitis: quantification of vascular signal and analysis of interobserver variability. Skeletal Radiol. 38(5), 467–472 (2009)
Kamishima, T., Sagawa, A., Tanimura, K., Shimizu, M., Matsuhashi, M., Shinohara, M., Hagiwara, H., Henmi, M., Narita, A., Terae, S., Shirato, H.: Semi-quantitative analysis of rheumatoid finger joint synovitis using power Doppler ultrasonography: when to perform follow-up study after treatment consisting mainly of antitumor necrosis factor alpha agent. Skeletal Radiol. 39(5), 457–465 (2010)
Albrecht, K., Muller-Ladner, U., Strunk, J.: Quantification of the synovial perfusion in rheumatoid arthritis using Doppler ultrasonography. Clin. Exp. Rheumatol. 25(4), 630 (2007)
Hoskins, P., Martin, K., Thrush, A.: Diagnostic Ultrasound: Physics and Equipment. Cambridge University Press, Cambridge (2010)
Buades, A., Coll, B., Morel, J.M.: A review of image denoising algorithms, with a new one. Multiscale Model Simul. 4(2), 490–530 (2006)
Strong, D., Chan, T.: Edge-preserving and scale-dependent properties of total variation regularization. Inverse Prob. 19, 165–187 (2003)
Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. PAMI 12(7), 629–639 (1990)
Smolka, B., Plataniotis, K.N., Venetsanopoulos, A.N.: Nonlinear techniques for color image processing, in nonlinear signal and image processing. In: Barner, K.E., Arce, G.R. (eds.) Theory, Methods and Applications, pp. 445–505. CRC Press, Boca Raton (2004)
Smolka, B., Venetsanopoulos, A.N.: Noise reduction and edge detection in color images. In: Lukac, R., Plataniotis, K.N. (eds.) Color Image Processing: Methods and Applications, pp. 75–100. CRC Press, Boca Raton (2007)
Barner, K.E., Arce, G.R.: Nonlinear Signal and Image Processing. CRC Press, Boca Raton (2004)
Malik, K., Smolka, B.: Modified bilateral filter for the restoration of noisy color images. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P., Zemčík, P. (eds.) ACIVS 2012. LNCS, vol. 7517, pp. 72–83. Springer, Heidelberg (2012)
McDicken, W.N., Anderson, T.: The difference between colour Doppler velocity imaging and power Doppler imaging. Eur. J. Echocardiogr. 3, 240–244 (2002)
Naredo, E., Collado, P., Cruz, A., et al.: Longitudinal power Doppler ultrasonographic assessment of joint inflammatory activity in early rheumatoid arthritis: predictive value in disease activity and radiologic progression. Arthritis Rheum. 57, 116–124 (2007)
Qvistgaard, E., Rogind, H., Torp-Pedersen, S., et al.: Quantitative ultrasonography in rheumatoid arthritis: evaluation of inflammation by Doppler technique. Ann. Rheum. Dis. 60, 690–693 (2001)
Cloutier, G., Qin, Z., Garcia, D., et al.: Assessment of arterial stenosis in a flow model with power Doppler angiography: accuracy and observations on blood echogenicity. Ultrasound Med. Biol. 26(9), 1489–1501 (2000)
van Riel, P.L., Schumacher, H.R.: How does one assess early rheumatoid arthritis in daily clinical practice? Best Pract. Res. Clin. Rheumatol. 15, 67–76 (2001)
Osher, S., Sethian, J.A.: Level Set Methods: Evolving Interfaces in Geometry, Fluid Mechanics, Computer Vision and Materials Sciences. Cambridge University Press, Cambridge (1996)
Ojala, T., Pietikinen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recogn. 29, 51–59 (1996)
Kawulok, M., Kawulok, J., Smolka, B.: Discriminative textural features for image and video colorization. In: IEICE Transactions on Information and Systems, vol. E95-D, no. 7 (2012). Kawulok, M., Smolka, B.: Texture-adaptive image colorization framework. EURASIP J. Adv. Sig. Process. 99 (2011)
Chang, W., Zwicker, M.: Automatic registration for articulated shapes. In: Computer Graphics Forum (Proceedings of SGP 2008), vol. 27, p. 5 (2008)
Mateus, D., Horaud, R.P., Knossow, D., Cuzzolin, F., Boyer, E.: Articulated shape matching using Laplacian eigenfunctions and unsupervised point registration. In: Proceedings of IEEE CVPR, June 2008
Segen, J.: Inference of stochastic graph models for 2D and 3D shape. In: Proceedings of NATO Advanced Research Workshop on Shape in Picture, Driebergen, Netherlands (1992)
Schapire, R.E.: The boosting approach to machine learning: an overview. In: Workshop on Nonlinear Estimation and Classification, Mathematical Sciences Research Institute (MSRI), Berkeley, CA (2003)
Kulbacki, M., Segen, J., Habela, P., Janiak, M., Knieć, W., Fojcik, M., Mielnik, P., Wojciechowski, K.: Collaborative tool for annotation of synovitis and assessment in ultrasound images. In: Chmielewski, L.J., Kozera, R., Shin, B.-S., Wojciechowski, K. (eds.) ICCVG 2014. LNCS, vol. 8671, pp. 364–373. Springer, Heidelberg (2014)
Popowicz, A., Smolka, B.: Bilateral filtering based biomedical image colorization. In: Tavares, J.M.R.S., Natal, A.M.J. (eds.) Computational Vision and Medical Image Processing, pp. 163–169. CRC Press, Boca Raton (2016)
Radlak, K., Radlak, N., Smolka, B.: Automatic detection of bones based on the confidence map for Rheumatoid Arthritis analysis. In: Tavares, J.M.R.S., Natal, A.M.J. (eds.) Computatiion Vision and Medical Imge Processing, pp. 215–220. CRC Press, Boca Raton (2016)
Nurzynska, K., Smolka, B.: Automatic finger joint synovitis1 localization in ultrasound images. In: Proceedings of SPIE, vol. 9897, pp. 98970N–98970N-11, April 2016. doi:10.1117/12.2227638
Acknowledgement
This research obtained funding from the Norwegian Financial Mechanism 2009–2014 under Project Contract No. Pol-Nor/204256/16/2013. The ultrasound images for the MEDUSA project were created at the Section for Rheumatology; Department for Neurology, Rheumatology and Physical Medicine, 238 Central Hospital, Forde, Norway.
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Wojciechowski, K. et al. (2016). A Machine-Learning Approach to the Automated Assessment of Joint Synovitis Activity. In: Nguyen, N., Iliadis, L., Manolopoulos, Y., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2016. Lecture Notes in Computer Science(), vol 9876. Springer, Cham. https://doi.org/10.1007/978-3-319-45246-3_42
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