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Medical Image Segmentation Using Improved Affinity Propagation

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Computational Modeling of Objects Presented in Images. Fundamentals, Methods, and Applications (CompIMAGE 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10149))

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Abstract

Affinity Propagation (AP) is an effective clustering method with a number of advantages over the commonly used k-means clustering. For example, it does not need to specify the number of clusters in advance, and can handle clusters with general topology, which makes it uniquely suitable for medical image segmentation as most of the objects in medical images are not roundly shaped. One factor hampering its applications is its relatively slow speed, especially for large-size images. To overcome this difficulty, we propose in this paper an Improved Affinity Propagation (IMAP) method with several improved features. Particularly, our IMAP method can adaptively select the key parameter p in AP according to the medical image gray histogram, and thus can greatly speed up convergence. Experimental results suggest that IMAP has a higher image entropy, lower class square error contrast, and shorter runtime than the AP algorithm.

This work is supported in part by Overseas Training Program for Outstanding Young Teachers and Principals of Universities in Jiangsu Province, Natural Science Fund Project of College in Jiangsu Province (14KJB520039), the National Nature Science Foundation of China (No. 61379101), and the Basic Research Program (Natural Science Foundation) of Jiangsu Province of China (No. BK20130209).

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Authors and Affiliations

  1. School of Medical Information, Xuzhou Medical College, Xuzhou, China

    Hong Zhu, Junfeng Hu & Jing Chen

  2. Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, USA

    Jinhui Xu

Authors
  1. Hong Zhu
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  2. Jinhui Xu
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  3. Junfeng Hu
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  4. Jing Chen
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Corresponding author

Correspondence to Hong Zhu .

Editor information

Editors and Affiliations

  1. State University of New York at Fredonia , Fredonia, New York, USA

    Reneta P. Barneva

  2. State University of New York , Buffalo, New York, USA

    Valentin E. Brimkov

  3. Universidade do Porto , Porto, Portugal

    João Manuel R.S. Tavares

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Zhu, H., Xu, J., Hu, J., Chen, J. (2017). Medical Image Segmentation Using Improved Affinity Propagation. In: Barneva, R., Brimkov, V., Tavares, J. (eds) Computational Modeling of Objects Presented in Images. Fundamentals, Methods, and Applications. CompIMAGE 2016. Lecture Notes in Computer Science(), vol 10149. Springer, Cham. https://doi.org/10.1007/978-3-319-54609-4_15

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  • DOI: https://doi.org/10.1007/978-3-319-54609-4_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54608-7

  • Online ISBN: 978-3-319-54609-4

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