Abstract
Parts of a joint anatomy, such as bones or the joint center can be robustly identified in an ultrasound image with the help of an articulated or structural model. Such a model is a structure of parts that represent the bones and skin as polygonal chains and the join as a point, where the parts remain within specified geometric relations. The parts are identified by registration or a match of a structural description derived from the ultrasound image with the articulated model. To account for anatomical differences between the subjects, a library of joint models must be constructed, each model representing a class of joints, where all models together cover the range of possible anatomies. A new method of unsupervised learning is proposed for constructing the library of joint models by clustering structural descriptions computed from image annotations. The clustering method uses an inter-model distance measure defined as a minimum of the objective function that measures a discrepancy between structural descriptions. The objective function is minimized through a search for a best match between two structural descriptions. The method presentation is illustrated with the results of its application to ultrasound images of finger joints.
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Acknowledgments
The research leading to these results has received funding from the Polish-Norwegian Research Programme operated by the National Centre for Research and Development under the Norwegian Financial Mechanism 2009–2014 in the frame of Project Contract No. Pol-Nor/204256/16/2013.
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Segen, J., Wereszczyński, K., Kulbacki, M., Bąk, A., Wojciechowska, M. (2016). Learning Articulated Models of Joint Anatomy from Utrasound Images. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, TP. (eds) Intelligent Information and Database Systems. ACIIDS 2016. Lecture Notes in Computer Science(), vol 9622. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49390-8_45
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DOI: https://doi.org/10.1007/978-3-662-49390-8_45
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