Automatic histogram-based initialization of K-means clustering in CT
Tian M, Yang Q, Maier A, Schasiepen I, Maass N, Elter M (2013)
Publication Type: Conference contribution
Publication year: 2013
Journal
Publisher: Kluwer Academic Publishers
Pages Range: 277-282
Conference Proceedings Title: Informatik aktuell
Event location: Heidelberg
ISBN: 9783642364792
DOI: 10.1007/978-3-642-36480-8_49
Abstract
K-means clustering [1] has been widely used in various applications. One intrinsic limitation in K-means clustering is that the choice of initial clustering centroids may highly influence the performance of the algorithm. Some existing K-means initialization algorithms could generally achieve good results. However, in certain cases, such as CT images that contain several materials with similar gray-levels, such existing initialization algorithms will lead to poor performance in distinguishing those materials. We propose an automatic K-means initialization algorithm based on histogram analysis, which manages to overcome the aforementioned deficiency. Results demonstrate that our method achieves high efficiency in terms of finding starting points close to ground truth so that offers reliable segmentation results for CT images in aforementioned situation.
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How to cite
APA:
Tian, M., Yang, Q., Maier, A., Schasiepen, I., Maass, N., & Elter, M. (2013). Automatic histogram-based initialization of K-means clustering in CT. In Hans-Peter Meinzer, Thomas Martin Deserno, Heinz Handels, Thomas Tolxdorff (Eds.), Informatik aktuell (pp. 277-282). Heidelberg, DE: Kluwer Academic Publishers.
MLA:
Tian, Mengqiu, et al. "Automatic histogram-based initialization of K-means clustering in CT." Proceedings of the Workshops Bildverarbeitung fur die Medizin: Algorithmen - Systeme - Anwendungen, BVM 2013 - Workshop on Image Processing for Medicine: Algorithms - Systems - Applications, BVM 2013, Heidelberg Ed. Hans-Peter Meinzer, Thomas Martin Deserno, Heinz Handels, Thomas Tolxdorff, Kluwer Academic Publishers, 2013. 277-282.
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