Summary
This paper provides — through the use of cluster analysis — objective confirmation of the relevance of texture description applied to vascular tree images. Moreover, it is shown that unsupervised selection of significant texture parameters in the datasets corresponding to noisy images becomes feasible if the search for relevant attributes is guided by the clustering stability–based optimization criterion.
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Klepaczko, A., Kocinski, M., Materka, A. (2009). Cluster Analysis in Application to Quantitative Inspection of 3D Vascular Tree Images. In: Kurzynski, M., Wozniak, M. (eds) Computer Recognition Systems 3. Advances in Intelligent and Soft Computing, vol 57. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-93905-4_11
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DOI: https://doi.org/10.1007/978-3-540-93905-4_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-93904-7
Online ISBN: 978-3-540-93905-4
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