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Medical Image Segmentation Using Improved Affinity Propagation

  • Hong ZhuEmail author
  • Jinhui Xu
  • Junfeng Hu
  • Jing Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10149)

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.

Keywords

Medical image segmentation Affinity propagation Gray level histogram 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Medical InformationXuzhou Medical CollegeXuzhouChina
  2. 2.Department of Computer Science and EngineeringState University of New York at BuffaloBuffaloUSA

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