Abstract
One of the common methods for medical diagnosis is Magnetic Resonance Imaging (MRI), a safe, non-invasive method. During each imaging session a patient’s position may be different, therefore comparison of two sequences can become difficult. The primary goal of this work is preparation of an optimal algorithm for co-registration of T1 and T2 weighted MRI images. To adjust co-registration sensitivity, different preprocessing methods to perform normalizations and edge detection were used. The obtained results allow to increase quality of the co-registration process.
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References
Bielecka, M., Bielecki, A., Korkosz, M., Skomorowski, M., Wojciechowski, W., Zieliński, B.: Application of shape description methodology to hand radiographs interpretation, vol. 6374, pp. 11–18 (2010)
Bzowski, P., Danch-Wierzchowska, M., Psiuk-Maksymowicz, K., Panek, R., Borys, D.: Rigid and non-rigid registration algorithm evaluation in MRI for breast cancer therapy monitoring, vol. 762, pp. 150–159 (2019)
D’Agostino, E., Maes, F., Vandermeulen, D., Suetens, P.: A viscous fluid model for multimodal non-rigid image registration using mutual information. Med. Image Anal. 7(4), 565–575 (2003)
Kociołek, M., Piórkowski, A., Obuchowicz, R., Kamiński, P., Strzelecki, M.: Lytic region recognition in hip radiograms by means of statistical dominance transform. In: ICCVG. LNCS, vol. 11114, pp. 349–360. Springer (2018)
Nurzynska, K., Smolka, B.: Segmentation of finger joint synovitis in ultrasound images. In: 2016 IEEE ICCE, pp. 335–340. IEEE (2016)
Pietka, E., Pospiech, S., Gertych, A., Cao, F., Huang, H., Gilsanz, V.: Computer automated approach to the extraction of epiphyseal regions in hand radiographs. J. Digit. Imaging 14(4), 165–172 (2001)
Piorkowski, A.: A statistical dominance algorithm for edge detection and segmentation of medical images. In: Information Technologies in Medicine. AISC, vol. 471, pp. 3–14. Springer (2016)
Tadeusiewicz, R., Ogiela, M.R.: Picture languages in automatic radiological palm interpretation. Int. J. Appl. Math. Comput. Sci. 15, 305–312 (2005)
Viola, P.: Alignment by maximization of mutual information. A.I. Technical Report No. 1548 (1995)
Włodarczyk, J., Czaplicka, K., Tabor, Z., Wojciechowski, W., Urbanik, A.: Segmentation of bones in magnetic resonance images of the wrist. Int. J. Comput. Assist. Radiol. Surg. 10(4), 419–431 (2015)
Włodarczyk, J., Wojciechowski, W., Czaplicka, K., Urbanik, A., Tabor, Z.: Fast automated segmentation of wrist bones in magnetic resonance images. Comput. Biol. Med. 65, 44–53 (2015)
Xiong, G.: Local Normalization (2005). www.mathworks.com/matlabcentral/fileexchange/8303-local-normalization
Zieliński, B., Skomorowski, M., Wojciechowski, W., Korkosz, M., Sprȩżak, K.: Computer aided erosions and osteophytes detection based on hand radiographs. Pattern Recogn. 48(7), 2304–2317 (2015)
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Bzowski, P., Borys, D., Guz, W., Obuchowicz, R., Piórkowski, A. (2020). Evaluation of the MRI Images Matching Using Normalized Mutual Information Method and Preprocessing Techniques. In: Choraś, M., Choraś, R. (eds) Image Processing and Communications. IP&C 2019. Advances in Intelligent Systems and Computing, vol 1062. Springer, Cham. https://doi.org/10.1007/978-3-030-31254-1_12
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DOI: https://doi.org/10.1007/978-3-030-31254-1_12
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